All your study material, one place

1. Module 1: Fundamentals of Product Management
2. Module 2: Product Strategy and Strategic Thinking
3. Module 3: Problem Discovery and Market Research
4. Module 4: Problem Framing and Prioritization
5. Module 5: UX and Design Collaboration
6. Module 6: Effective Communication and Stakeholder Management
7. Module 7: Product Analytics and Metrics
8. Module 8: Product Launch, Growth, and Monetization
9. Module 9: Tech 101 and System Design for PMs
10. Module 10: LLMs, RAGs, and AI Foundations
11. Module 11: Vibe Coding for Product Managers
12. Module 12: Agentic AI and AI Systems
13. Module 13: AI Tools Landscape and Your Starter Stack
14. Module 14: Claude for Daily PM Work: Basic to Advanced
15. Module 15: Claude Code: Basic to Advanced
16. Module 16: Lovable: Basic to Advanced
17. Module 17: Replit and Other AI App Builders
18. Module 18: Figma and AI Design Tools
19. Module 19: Notion, PRD Tools and AI Documentation
20. Module 20: Automation with Zapier, Make and n8n
21. Module 21: AI Chatbots for Business: WhatsApp, Slack and Web
22. Module 22: Voice Agents for Businesses
23. Module 23: Learning Paths, Courses and Communities

• Frameworks library
• Case studies
• Glossary
• Interview preparation
• Appendix

AI builder tools let PMs and non-coders research, write, prototype, build websites and apps, and connect tools together. This course shows what each tool is best at and walks you through real builds step by step.

1. Basic Class 1: Choosing the right AI toolKnow the four kinds of AI tools and when to use each.
2. Basic Class 2: Claude for PMs: what you can do and howUse Claude's Projects, Artifacts, research, connectors and Skills in daily PM work.
3. Basic Class 3: ChatGPT for PMs: Projects, GPTs, Canvas, research and agentUse ChatGPT's main features for research, documents, data and automation.
4. Basic Class 4: Gemini for PMs: Gemini app, Gems, Deep Research, NotebookLM, Workspace, AI StudioUse Google's AI tools, especially if your team lives in Google Workspace.
5. Intermediate Class 5: Lovable: from idea to a working app (no code)Build and publish an app with Lovable, including a database, login and your own domain.
6. Intermediate Class 6: Claude Code and OpenAI Codex: coding agents for PMsUse coding agents to build and change real websites and apps, safely.
7. Intermediate Class 7: Tutorial: create a website, add pages and publish on your domainFollow one complete path from idea to a live site you own.
8. Intermediate Class 8: Connect your tools: Zapier, Make, n8n, webhooks and MCPMake your website and tools talk to each other.
9. Advanced Class 9: Connect other websites and servicesEmbed, integrate and call other websites safely.
10. Advanced Class 10: PM workflows end to end and capstoneCombine tools into a real product workflow.

Webhooks tell your system the moment something happens; triggers decide when automations run; browser automation does the clicking when there's no API; and the Playwright MCP lets an AI assistant like Claude use a real browser for you. Together they're the most-used web automation stack in 2026.

1. Basic Class 1: The web-automation stack: APIs, webhooks, triggers, browsersSee how the pieces fit, and pick the right one for each job.
2. Basic Class 2: Webhooks explained (with real examples)Understand webhooks and receive your first one.
3. Intermediate Class 3: Building reliable webhook receiversVerify, process and retry webhooks safely in production.
4. Basic Class 4: Triggers: schedules, events, polling and manualChoose how and when automations start.
5. Intermediate Class 5: Playwright for browser automationAutomate any website reliably with Playwright.
6. Intermediate Class 6: Playwright MCP: let Claude and other AI assistants use a browserInstall and use the Playwright MCP server with Claude Code, Claude Desktop, Cursor and VS Code.
7. Intermediate Class 7: Other browser-automation frameworks and MCPsKnow the alternatives and when each wins.
8. Advanced Class 8: AI browser agents and cloud browsersUse AI-native automation tools and hosted browsers at scale.
9. Advanced Class 9: Workflow platforms: n8n, Zapier, Make, Pipedream, RPA and scrapersRun webhooks, triggers and browser steps as complete workflows.
10. Advanced Class 10: Production: security, legality, monitoring, evals and capstoneRun web automations safely and prove they work.

Prompt engineering is how you ask; context engineering is what the AI gets to see when it answers: your instructions, examples, documents, data, memory and tools. Getting both right is the first and most useful AI skill.

1. Basic Class 1: From prompts to contextSee why the information you give the AI matters more than clever wording.
2. Basic Class 2: Prompt patterns that workUse proven patterns: examples, step-by-step, roles, critique and iteration.
3. Intermediate Class 3: Structured outputs: JSON, tables and fixed sectionsGet predictable output other tools and people can use directly.
4. Intermediate Class 4: Task decomposition and chainingBreak big jobs into steps the AI can do reliably.
5. Intermediate Class 5: Long context: compression, summaries and memoryKeep long projects accurate when chats and documents get large.
6. Intermediate Class 6: Documents, PDFs, scans and OCRGet reliable information out of PDFs, slides, spreadsheets and images.
7. Advanced Class 7: Source attribution, fact-checking and knowledge graphsMake AI outputs traceable to evidence and organise knowledge by relationships.
8. Advanced Class 8: Context engineering for products and agentsDesign what your AI feature or agent sees, and test it.

A skill is a reusable method you teach an AI once (instructions, templates, examples, scripts) so it does a recurring task the same good way every time. A tool gives the AI access or actions; memory and evals are parts of the system around it.

1. Basic Class 1: Skills, tools, memory and evals: the systemKnow the parts of an AI assistant and where a skill fits.
2. Basic Class 2: Templates: the foundation of every skillDesign templates that make outputs consistent and complete.
3. Intermediate Class 3: Where to build skills: Projects, GPTs, Gems and Agent SkillsChoose the right place to package a skill.
4. Intermediate Class 4: Writing great skill instructionsWrite instructions that produce consistent, high-quality results.
5. Intermediate Class 5: A PM skill libraryBuild a set of skills that covers your core PM work.
6. Advanced Class 6: Connecting skills to sources and toolsGround skills in your documents and let them act through tools.
7. Advanced Class 7: Testing skills with evalsProve a skill works and stays working.
8. Advanced Class 8: Project: case study → PRD → presentation skillBuild a portfolio-ready reusable skill end to end (e.g. for a VitaFit-style assignment).

A second brain is a personal knowledge system outside your head: notes, documents, meetings and ideas stored in one trusted place. With AI, it becomes something you can talk to: ask questions across everything you've saved and get answers with sources.

1. Basic Class 1: What a second brain is, and why AI changes itUnderstand the idea, the PARA and CODE methods, and what AI adds.
2. Basic Class 2: Capture: getting things into the systemSet up fast, low-friction capture from phone, browser, reading and voice.
3. Basic Class 3: Organise and distilTurn a pile of captures into findable, useful notes.
4. Intermediate Class 4: Ask AI across your notesUse NotebookLM, Claude and ChatGPT Projects, Notion AI and Obsidian plugins to question your knowledge.
5. Intermediate Class 5: Context engineering and AI memoryGive AI the right context about you and your work, and manage what it remembers.
6. Intermediate Class 6: Pipelines: meetings, email, reading and learningAutomatically feed your second brain from meetings, email and courses.
7. Advanced Class 7: Second brains for PMs, students and teamsApply the system to product work, exam prep and team knowledge.
8. Advanced Class 8: Build your own: MCP, automation and evaluationConnect AI assistants to your notes with MCP and automation, and make sure answers stay accurate.

An eval is a repeatable test that measures how well an AI feature does its job on a fixed set of examples, with a clear way to score each answer. It's the quality bar, the launch gate and the product spec for AI.

1. Basic Class 1: What an eval is, and why PMs own itUnderstand evals as the unit test, the quality bar and the product spec for AI features.
2. Basic Class 2: Defining success: criteria, rubrics and failure modesTurn a fuzzy goal ('helpful answers') into criteria anyone can score the same way.
3. Basic Class 3: Building the eval dataset (golden set)Create a test set that represents real users, including the hard and risky cases.
4. Intermediate Class 4: Grading methods: code, humans and LLM-as-judgeChoose the cheapest grader that measures what matters, and know when to use humans.
5. Intermediate Class 5: Error analysis: finding what actually breaksRead outputs systematically, name the failure modes and fix the biggest ones first.
6. Intermediate Class 6: Evaluating RAG (retrieval-augmented generation)Measure retrieval and generation separately, so you know which half to fix.
7. Advanced Class 7: Evaluating agents and tool useMeasure multi-step AI agents on outcomes, the path they took, cost and safety.
8. Advanced Class 8: Safety, responsible AI and red-teamingTest for harm, privacy leaks, bias and jailbreaks before attackers and journalists do.
9. Advanced Class 9: Online evals: A/B tests, monitoring and regression gatesConnect offline scores to real user value and catch quality drops in production.
10. Advanced Class 10: Eval ops: tools, cost, the AI PRD and your capstoneRun evals as a product practice: tooling, budgets, documentation and team rituals.

Browser automation means getting software, and now AI, to use websites for you: open pages, click, type, fill forms, collect data and complete tasks. Classic scripts follow fixed steps; AI browser agents take a goal in plain English and decide each step themselves.

1. Basic Class 1: What browser automation is (and when to use it)Understand the three generations of web automation and pick the right one for a task.
2. Basic Class 2: No-code browser automationAutomate simple web tasks without writing code.
3. Intermediate Class 3: Playwright for PMs and analystsRead and write a simple browser script, so you can work with engineers and automate small tasks.
4. Intermediate Class 4: AI browser agents and computer useUnderstand how AI agents use browsers and computers, and how to give them good instructions.
5. Intermediate Class 5: Deep research agents and web research workflowsUse AI research agents for market and competitor research, and verify what they find.
6. Advanced Class 6: Real workflows: PM, analyst, marketing and opsDesign end-to-end automations that combine agents, scripts and no-code tools.
7. Advanced Class 7: Safety, security, ethics and the lawPrevent the main risks of browser agents: injection, wrong actions, data leaks and rule-breaking.
8. Advanced Class 8: Evaluating and productising browser agentsMeasure agent quality and write a PRD for a browser-agent feature; complete the final project.

Orchestration is how AI steps, tools and people are connected into a workflow that runs reliably; monitoring is how you know it keeps working. Start with a simple workflow and add agents only where they are needed.

1. Basic Class 1: Workflows vs agents: start simpleKnow when a fixed workflow is enough and when an agent is worth it.
2. Basic Class 2: Core patterns: chaining, routing and parallelisationUse the three most common orchestration patterns.
3. Intermediate Class 3: Orchestrator-workers and evaluator-optimizerUse the two most powerful agentic patterns safely.
4. Intermediate Class 4: State, checkpoints and long-running workMake workflows resumable and traceable.
5. Intermediate Class 5: Human-in-the-loop, approvals and error recoveryPut people at the right points and recover from failures safely.
6. Advanced Class 6: Multi-agent systemsKnow when splitting work across agents helps, and how to coordinate them.
7. Advanced Class 7: Triggers, scheduling and cost controlRun workflows automatically and within budget.
8. Advanced Class 8: Monitoring, observability and the final projectKnow when a workflow breaks or degrades, and fix it fast.

AI product strategy is deciding which problems AI should solve for your users and business, how you'll win (data, distribution, workflow, trust), and what to build, buy or partner for, before writing a single PRD.

1. Basic Class 1: Where AI creates valueSeparate real AI opportunities from hype.
2. Basic Class 2: Moats and data flywheelsUnderstand what makes an AI product hard to copy.
3. Intermediate Class 3: Build vs buy vs partner, and choosing modelsMake sourcing and model decisions with clear trade-offs.
4. Intermediate Class 4: Assessing and prioritising AI opportunitiesScore AI ideas on value, feasibility and risk.
5. Advanced Class 5: AI roadmaps and portfolio of betsPlan AI work under uncertainty.
6. Advanced Class 6: Case studies and final projectApply the strategy tools end to end.

AI UX is designing products where outputs are probabilistic: helping users understand what AI can do, trust it the right amount, correct it easily, and recover when it's wrong.

1. Basic Class 1: Principles of AI UXDesign for uncertainty and the right level of trust.
2. Basic Class 2: Interaction patternsPick the right pattern for the job.
3. Intermediate Class 3: Trust, explainability and citationsHelp users judge AI output quickly.
4. Intermediate Class 4: Feedback, control and personalisationTurn user actions into learning signals.
5. Advanced Class 5: Failure, safety and handoff designDesign what happens when AI can't or shouldn't answer.
6. Advanced Class 6: Testing AI UX and final projectValidate AI experiences with users and evals.

AI features have real running costs on every use, so pricing must cover cost per task while matching the value customers get; that's why AI pricing is shifting from per-seat to usage and outcome models.

1. Basic Class 1: AI cost structure and unit economicsKnow what each AI task costs.
2. Basic Class 2: Pricing modelsCompare seat, usage, credits, outcome and hybrid pricing.
3. Intermediate Class 3: Packaging AI featuresDecide free, paid and add-on AI.
4. Intermediate Class 4: Protecting marginsReduce AI cost without hurting quality.
5. Advanced Class 5: Go-to-market for AI productsLaunch and sell AI with proof.
6. Advanced Class 6: Final projectPrice and package a real AI product.

Before LLMs, most AI in products was machine learning that predicts, classifies, ranks or recommends (fraud scores, feeds, ETAs). PMs still own these products: the problem, the data, the metric trade-offs and the lifecycle.

1. Basic Class 1: ML problem typesRecognise which ML problem you're solving.
2. Basic Class 2: Data and labellingGet the data an ML product needs.
3. Intermediate Class 3: Metrics and trade-offsChoose model metrics that match business costs.
4. Intermediate Class 4: Recommendations and personalisationUnderstand how recommenders work and are measured.
5. Advanced Class 5: Model lifecycle and MLOpsKeep models working after launch.
6. Advanced Class 6: Working with data scientists and final projectCollaborate well and ship an ML feature.

AI governance is the set of policies, reviews and controls that make sure AI is safe, fair, legal and accountable, so teams can ship faster with fewer surprises.

1. Basic Class 1: Principles and risksKnow the main responsible-AI principles and risks.
2. Basic Class 2: Regulations to knowUnderstand the main laws and frameworks at a PM level.
3. Intermediate Class 3: AI risk and impact assessmentAssess a feature before launch.
4. Intermediate Class 4: Policies and documentationWrite the documents governance needs.
5. Advanced Class 5: Operating governanceRun governance without slowing teams down.
6. Advanced Class 6: Final projectGovern a real AI feature.

A platform PM builds products that other teams, developers or partners build on: APIs, SDKs, internal platforms and ecosystems. The users are developers, and the product is how easily they succeed.

1. Basic Class 1: Platforms and APIs as productsUnderstand platform types and users.
2. Basic Class 2: Developer experience (DX)Make developers successful fast.
3. Intermediate Class 3: API design decisions for PMsDecide what matters in API contracts.
4. Intermediate Class 4: Ecosystems and partnersGrow a platform through others.
5. Advanced Class 5: Platform metricsMeasure platform health.
6. Advanced Class 6: AI platforms and final projectApply platform thinking to AI.

Product operations is the function that makes product teams run smoothly at scale: shared processes, tools, data and insight pipelines, so PMs spend time on decisions instead of admin.

1. Basic Class 1: What product ops isKnow the scope and value of product ops.
2. Basic Class 2: Processes and ritualsDesign lightweight rhythms.
3. Intermediate Class 3: Tools and dataRun the product tool stack and metrics.
4. Intermediate Class 4: Customer-insight pipelinesTurn feedback into prioritised insights.
5. Advanced Class 5: Launch management and governanceMake launches predictable.
6. Advanced Class 6: AI in product ops and final projectAutomate product ops with AI.

Multimodal AI understands and generates more than text: images, audio and video. It powers features like reading invoices from photos, voice assistants, product images and video explainers, along with new risks like deepfakes.

1. Basic Class 1: Multimodal basicsKnow what modern models can see, hear and create.
2. Basic Class 2: Vision understanding use casesUse AI to read and interpret images.
3. Intermediate Class 3: Image generation in productsUse generative images responsibly.
4. Intermediate Class 4: Voice and videoBuild with speech and video AI.
5. Advanced Class 5: Rights, safety, deepfakes and labellingManage the risks of AI media.
6. Advanced Class 6: Final projectShip a multimodal feature.

From the Excel window to dashboards: formatting, 100+ functions, lookups, pivot tables, advanced charts, Power Query, Power Pivot and AI in Excel.

1. 1. Getting Started with Excel for Data AnalysisBeginner 90 min Learn the Excel window, the words analysts use every day, how to lay out data so it can be analysed, the five data types Excel understands, and the shortcuts that save hours.PDF
2. 2. Data Entry, Data Validation & Data CleaningBeginner 90 min Stop bad data at the door with data validation and drop-down lists, then clean what already exists with Flash Fill, Text to Columns, Find & Replace, Go To Special, Remove Duplicates, Paste Special and Excel Tables.PDF
3. 3. Number, Custom & Conditional FormattingBeginner 100 min Make numbers readable and insights jump out. Learn cell and number formats, write your own custom number formats (including lakhs and crores, K and M, and colour codes), and use conditional formatting with built-in rules and your own formulas.PDF
4. 4. Formulas, Cell References & Aggregate FunctionsBeginner 110 min Write formulas with confidence. Understand operators and their order, relative, absolute and mixed references, and every aggregate function you need, from SUM and COUNTA to SMALL, LARGE, SUMIFS, COUNTIFS, MAXIFS, SUBTOTAL and AGGREGATE.PDF
5. 5. Text Functions, Logical Functions & IFBeginner 120 min Extract, clean, combine and reformat text with LEFT, RIGHT, MID, LEN, FIND, SEARCH, CONCAT, TEXTJOIN, TEXT and the new TEXTSPLIT/TEXTBEFORE/TEXTAFTER. Then make decisions with AND, OR, NOT, IF, nested IF, IFS, SWITCH and IFERROR.PDF
6. 6. Sorting, Filtering & Dynamic ArraysBeginner–Intermediate 110 min Organise data with single and multi-level sorts and custom orders, find what matters with AutoFilter and Advanced Filter, and then let formulas do it for you with the dynamic array functions UNIQUE, SORT, SORTBY, FILTER and SEQUENCE, plus the power tool SUMPRODUCT.PDF
7. 7. Lookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCHIntermediate 120 min Bring data together from different tables. Master VLOOKUP and HLOOKUP (exact and approximate match), the modern XLOOKUP, the flexible INDEX + MATCH combination (including two-way and reverse lookups), OFFSET, consolidating several sheets, CHOOSE, and how to troubleshoot #N/A.PDF
8. 8. Pivot Tables from Basic to AdvancedIntermediate 150 min Summarise thousands of rows in seconds. Build pivot tables, understand the pivot cache and the four areas, change value field settings, show values as % of total, MoM and YoY change and running totals, group dates and numbers into bins, add calculated fields, slicers and timelines, and solve the Video Game Sales case study.PDF
9. 9. Date, Time & Number Functions and Named RangesIntermediate 120 min Work confidently with dates and times (DATE, EOMONTH, EDATE, WORKDAY, NETWORKDAYS, WEEKDAY, WEEKNUM, DATEDIF, fiscal quarters), clean up numbers with INT, MOD, ROUND, CEILING and FLOOR, and make formulas readable with static and dynamic named ranges and LET.PDF
10. 10. Charts and Advanced ChartsIntermediate 130 min Choose the right chart for every question, build and format column, bar, line, area, pie, scatter and combo charts, add sparklines, and create the advanced business charts taught in class: thermometer, Pareto, population pyramid and waterfall, plus histogram, box plot, treemap and funnel.PDF
11. 11. Interactive Dashboard: Superstore Case StudyIntermediate–Advanced 150 min Build a complete, interactive sales dashboard from the Superstore data, from business questions and KPIs to pivot charts, slicers, a timeline, KPI cards and a dynamic title. Then protect it (sheet, workbook, allowed ranges), document it with comments and notes, and audit formulas with Evaluate Formula.PDF
12. 12. Power Query, Power Pivot, Analysis ToolPak & What-If AnalysisAdvanced 150 min Automate cleaning with Power Query (import, transform, unpivot, merge, append, group), model several tables with Power Pivot and DAX, get descriptive statistics and regression from the Analysis ToolPak, plan with Goal Seek, Data Tables, Scenario Manager and Solver, and meet LAMBDA, Python in Excel and macros.PDF

Relational databases, ER diagrams and keys, DDL/DML, filtering, aggregation, joins, set operators, subqueries, window functions, CTEs, views, procedures and performance.

1. 1. Databases, RDBMS & SQL BasicsBeginner 100 min Understand what a database is, how relational databases organise data into tables, the vocabulary of SQL, the four families of SQL statements (DDL, DML, DCL, TCL), how SQL compares with Excel, and how to install SQL Server and SSMS and run your first query.PDF
2. 2. ER Diagrams, Relationships, Keys & NormalisationBeginner 110 min Design databases the way professionals do. Draw ER diagrams, read crow's-foot notation, model one-to-one, one-to-many and many-to-many relationships, master every type of key (super, candidate, primary, alternate, composite, foreign, surrogate) and normalise tables to 3NF.PDF
3. 3. Data Types, Operators, NULLs, Variables & CommentsBeginner 100 min Choose the right SQL Server data type for every column, use arithmetic, comparison, logical and string operators correctly, understand NULL and three-valued logic, declare variables, follow naming rules and write readable, commented SQL.PDF
4. 4. DDL, DML & Constraints: CREATE, ALTER, INSERT, UPDATE, DELETEBeginner 120 min Create and change tables with DDL (CREATE, ALTER, DROP, TRUNCATE), add, change and remove rows with DML (INSERT, UPDATE, DELETE, MERGE), protect data quality with constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT, IDENTITY) and understand DELETE vs TRUNCATE vs DROP.PDF
5. 5. SELECT, WHERE, ORDER BY & Filtering OperatorsBeginner 120 min Retrieve exactly the rows and columns you need. Master SELECT, aliases with AS, DISTINCT, TOP, WHERE with comparison and logical operators, IN, BETWEEN, LIKE wildcards, IS NULL, ORDER BY, and the logical order in which SQL processes a query.PDF
6. 6. SQL Functions: Aggregate, Text, Date & NumberBeginner–Intermediate 120 min Use SQL Server's built-in functions to summarise, clean and reshape data. Aggregate functions (COUNT, SUM, AVG, MIN, MAX, STRING_AGG), text functions (LEN, LEFT, RIGHT, SUBSTRING, CHARINDEX, REPLACE, TRIM, UPPER, CONCAT), date functions (GETDATE, YEAR, MONTH, DATEPART, DATENAME, DATEADD, DATEDIFF, EOMONTH) and number functions (ROUND, CEILING, FLOOR, ABS, POWER, SQRT), plus CAST and CONVERT.PDF
7. 7. GROUP BY, HAVING, CASE WHEN, ROLLUP, CUBE & COALESCEIntermediate 130 min Turn rows into business summaries. Group with GROUP BY, filter groups with HAVING, create conditional columns and buckets with CASE WHEN, pivot data with conditional aggregation, and add subtotals and grand totals with ROLLUP, CUBE and GROUPING SETS, cleaning the labels with COALESCE and GROUPING().PDF
8. 8. Joins and a Retail Case StudyIntermediate 150 min Combine tables with INNER, LEFT, RIGHT, FULL OUTER, CROSS and SELF joins, find missing matches with anti-joins, avoid the classic join mistakes (row explosion, filters in ON vs WHERE), and answer a full set of business questions on the retail database.PDF
9. 9. Set Operators: UNION, UNION ALL, INTERSECT & EXCEPTIntermediate 80 min Stack and compare query results. Append data with UNION ALL, remove duplicates with UNION, find common rows with INTERSECT and differences with EXCEPT, follow the rules for compatible columns, and know when to use a set operator instead of a join.PDF
10. 10. Subqueries, Derived Tables & EXISTSIntermediate 120 min Put queries inside queries. Use scalar subqueries, multi-row subqueries with IN, ANY and ALL, correlated subqueries, EXISTS and NOT EXISTS, and derived tables (inline views) to answer questions that one level of SELECT cannot, and learn when a join, CTE or window function is better.PDF
11. 11. OFFSET-FETCH and Window Functions I: OVER, PARTITION BY & RankingIntermediate–Advanced 130 min Page through results with OFFSET and FETCH, then learn window functions, the most powerful analytics feature in SQL. Understand OVER and PARTITION BY, rank rows with ROW_NUMBER, RANK, DENSE_RANK and NTILE, solve top-N-per-group and de-duplication problems, and compare aggregate windows with GROUP BY.PDF
12. 12. Window Functions II: Running Totals, LEAD, LAG & FramesAdvanced 130 min Build time-series analytics in SQL. Use window frames (ROWS BETWEEN) for running totals and moving averages, LAG and LEAD for month-on-month and year-on-year growth, FIRST_VALUE and LAST_VALUE, cumulative percentages, PERCENT_RANK, CUME_DIST and PERCENTILE_CONT, and gaps-and-islands patterns.PDF
13. 13. CTEs, Views, Temporary Tables, Stored Procedures & FunctionsAdvanced 130 min Write queries other people can read and reuse. Use CTEs (including recursive CTEs for hierarchies and calendars), create views for reusable logic and security, use temp tables and table variables, and build stored procedures, user-defined functions, parameters and basic error handling in T-SQL.PDF
14. 14. Transactions, Indexes, Performance & SecurityAdvanced 110 min Make SQL safe and fast. Understand transactions and ACID (BEGIN, COMMIT, ROLLBACK, SAVE), isolation and locking, indexes (clustered, non-clustered, covering), how to read an execution plan, a checklist for writing fast queries, and permissions with GRANT, REVOKE and roles (DCL).PDF
15. 15. End-to-End SQL Case Study & Interview PracticeAdvanced 180 min Put everything together. Work through a complete analysis of the retail business (data quality, KPIs, trends, customer segmentation, retention, product and discount analysis) using joins, CTEs and window functions, then practise 25 classic SQL interview questions with model answers.PDF

ETL with Power Query, star-schema modelling, DAX measures and time intelligence, interactive reports, case studies, publishing, security and Copilot.

1. 1. Getting Started with Power BI & Your First HR ReportBeginner 120 min Understand what Power BI is and how its parts fit together, install Power BI Desktop, tour the three views, follow the ETL and analytics stages, and build a complete first report on HR data: cards, bar and column charts, a donut, slicers, a year column with DAX, groups and small multiples.PDF
2. 2. Power Query, Star Schema & Data Modelling (Adventure Cycles)Intermediate 140 min Prepare and model data the professional way. Clean data in Power Query, load a star schema (fact and dimension tables), create one-to-many relationships, understand filter direction, build and mark a date table, create hierarchies, and avoid the most common modelling mistakes, using the Adventure Cycles case study.PDF
3. 3. DAX Fundamentals: Measures, Filter Context & CALCULATEIntermediate 150 min Learn DAX, the formula language of Power BI. Understand calculated columns vs measures, row context vs filter context, write core aggregations and iterators (SUMX), master CALCULATE and FILTER, ALL/REMOVEFILTERS and ALLSELECTED, handle division safely, use variables, and build a library of business measures.PDF
4. 4. DAX Time Intelligence, Ranking, Parameters & Dynamic MeasuresAdvanced 150 min Make dashboards answer "compared with when?". Build year-to-date, month-to-date, same-period-last-year, month-on-month and year-on-year measures, rolling averages, rankings with RANKX and TOPN, what-if parameters, field parameters and dynamic measure switching, and know visual calculations and calculation groups.PDF
5. 5. Visuals, Filters, Drill-through, Tooltips, Buttons & Report DesignIntermediate 150 min Turn a model into an interactive, well-designed report. Choose visuals, format them (cards, matrix, charts, gauge, KPI, maps, images), use visual, page and report filters, edit interactions, drill-down and drill-through, custom tooltips, bookmarks and buttons, conditional formatting, themes and design principles.PDF
6. 6. Power BI Service, Connectivity Modes, Security & CapstoneAdvanced 150 min Take a report from Desktop to the people who need it. Understand Import, DirectQuery, Live connection, composite models and Direct Lake, publish to the Service, schedule refresh with a gateway, share through workspaces and apps, secure data with row-level security, tune performance, work with Copilot and Fabric, and complete the Adventure Cycles capstone.PDF

Tableau Public and Desktop: connecting data, dimensions and measures, every core chart, calculated fields, table calculations and LOD expressions, filters, parameters and sets, data modelling, dashboards, stories and publishing.

1. 1. Getting Started with Tableau: Interface, Data Connections and Your First ViewsBeginner 120 min Understand the Tableau product family, install Tableau Public, connect to the Superstore India data, read the Data Source page, learn dimensions vs measures and discrete vs continuous fields, master the Marks card and shelves, build and sort your first views, and save them to Tableau Public.PDF
2. 2. Charts in Tableau: From Bar and Line to Maps, Dual Axis, Histograms and ParetoBeginner 150 min Build every core chart in Tableau and know when to use each: bar, stacked and side-by-side bars, line and area, pie and donut, treemap, highlight table, scatter plot, histogram with bins, box plot, filled and symbol maps, dual-axis combo charts, bullet charts and a Pareto chart, all on the Superstore India data.PDF
3. 3. Calculations in Tableau: Calculated Fields, Table Calculations and LOD ExpressionsIntermediate 150 min Write calculated fields (row-level and aggregate), use string, date, number and logical functions, handle nulls, then master table calculations (running total, percent of total, difference and growth, rank, moving average, and Compute Using) and Level of Detail expressions (FIXED, INCLUDE, EXCLUDE), with every result checked against pandas.PDF
4. 4. Filters, Parameters, Sets, Groups and Hierarchies in TableauIntermediate 120 min Control what a view shows and let users interact with it. Learn every filter type and where it sits in the order of operations, context filters and Top N, groups, hierarchies, bins, sets (fixed, conditional, top) and combined sets, and parameters for dynamic Top N, measure swapping and what-if analysis.PDF
5. 5. Data Modelling in Tableau: Relationships, Joins, Unions, Blending, Extracts and Tableau PrepIntermediate 120 min Combine and shape data in Tableau. Understand the logical layer (relationships) and the physical layer (joins), when to union or blend, how row duplication inflates totals, live connections vs extracts, data interpreter and pivoting, and how Tableau Prep Builder cleans data in repeatable flows, using the Adventure Cycles star schema.PDF
6. 6. Dashboards, Actions, Stories and Publishing in Tableau (Capstone)Intermediate 180 min Turn sheets into an interactive dashboard and a data story. Plan the layout with containers, add filter, highlight, URL, parameter and set actions, design for phones, tell a story with Story Points, publish to Tableau Public or Cloud, and complete two capstones, a Superstore India sales dashboard and the video-game regional sales case study from class.PDF

Generative AI for analysts: prompting, AI vs GenAI vs agentic AI, AI over SQL (Metabase, MinusX), Copilot and Gemini in Excel, and office agents.

1. 1. Generative AI for Analysts: How It Works, Prompting & AI vs GenAI vs Agentic AIBeginner 110 min Understand what generative AI is and how large language models behave (tokens, context, temperature, hallucination), how AI, machine learning, deep learning, GenAI and agentic AI differ, and how to write prompts that give analysts accurate, checkable results, with reusable templates and privacy rules.PDF
2. 2. AI with SQL: Text-to-SQL, Docker, Metabase & MinusXIntermediate 130 min Use AI to write, explain and debug SQL safely. Learn how text-to-SQL works and where it fails, give the model the right schema, protect your database with read-only access, install Docker, run Metabase on your own data, connect an AI assistant such as MinusX, and validate every generated query.PDF
3. 3. AI in Excel & Office: Copilot, Gemini, Claude, Python in Excel & AgentsIntermediate 120 min Use AI where analysts work every day. Work with Copilot in Excel (formulas, insights, charts, the COPILOT function), Gemini in Google Sheets and Excel add-ins, Claude and other assistants for formulas and files, Python in Excel, and office agents that perform multi-step tasks, and learn how to prompt, verify and stay safe.PDF
4. 4. The AI-Assisted Analyst Workflow, Agents & Governance (Capstone)Intermediate 140 min Combine everything into one repeatable workflow: frame a question, brief the AI, analyse with code, verify, visualise and write the story. See how an analyst agent works (plan, tools, observe, repeat) with a runnable toy example, learn MCP and tool-calling basics, and finish with governance, ethics and a telecom-churn capstone.PDF

Python from zero: data types, control flow, data structures, functions, OOP, files, NumPy, pandas, visualisation, dates, SQL connections, EDA and DSA for data roles.

1. 1. Introduction to Python: Why, Install & Jupyter NotebooksBeginner 90 min Learn what programming and Python are, how Python compares with other languages, how code runs (interpreters, compilers, bytecode), the properties of Python, and set up your environment (Anaconda, VS Code, Jupyter, Google Colab). Write and run your first cells, with markdown, magic commands and good notebook habits.PDF
2. 2. Variables, Data Types, Type Conversion, print(), f-strings & input()Beginner 110 min Store and name values with variables, meet Python's core data types (int, float, str, bool, None, complex), convert between types, format output with print(), f-strings and .format(), and read user input with input() and sys.argv.PDF
3. 3. Operators, Strings, Indexing, Slicing & String MethodsBeginner 120 min Master Python's operators (arithmetic, comparison, logical, assignment, membership, identity, bitwise) and work with text: string indexing and slicing, immutability, every string method an analyst needs (split, join, strip, replace, find, startswith, case methods, isdigit…), escape sequences, and cleaning real messy text.PDF
4. 4. Control Flow: if, elif, else, Nested Conditions & matchBeginner 90 min Make programs decide. Learn indentation, if/elif/else, comparison and logical conditions, truthiness, conditional expressions (ternary), nested decisions, the match statement, and how Python's if maps to Excel's IF/IFS and SQL's CASE WHEN, with data-flavoured examples.PDF
5. 5. Loops: while, for, range, break, continue & PatternsBeginner 110 min Repeat work without repeating code. Learn while and for loops, range(), enumerate(), zip(), break, continue, the loop else clause, nested loops, accumulation, search and counting patterns, and common loop bugs; with an eye on when to replace loops with vectorised pandas/NumPy.PDF
6. 6. Lists: Creating, Slicing, Methods, Sorting & ComprehensionsBeginner 120 min Store and process ordered collections with lists. Index and slice them, add, remove and sort items, understand mutability, aliasing and copying (shallow vs deep), use nested lists, list comprehensions, and common patterns for cleaning and summarising data.PDF
7. 7. Tuples & Sets: Immutable Records, Unpacking and Set AlgebraBeginner 90 min Use tuples for fixed records and multiple return values, master unpacking and named tuples, and use sets for uniqueness, fast membership tests and set algebra (union, intersection, difference) on real customer data.PDF
8. 8. Dictionaries: Key-Value Data, Counting and GroupingBeginner 100 min Store and look up data by key. Create and update dictionaries, use get, setdefault, items, Counter and defaultdict, nest dictionaries, and convert between dictionaries, JSON-like records and DataFrames using the Superstore data.PDF
9. 9. Functions: Reusable Code, Arguments, Scope and DocstringsBeginner 100 min Package logic into reusable functions. Learn def, parameters and arguments (positional, keyword, default, *args, **kwargs), return values, scope, docstrings and type hints, plus testing small functions on real data.PDF
10. 10. Comprehensions, Lambda, map/filter and GeneratorsBeginner 100 min Write compact, readable Python. Learn list, set and dict comprehensions, lambda with sorted/min/max, map and filter, any and all, generators and itertools, and when plain loops or pandas are the better choice.PDF
11. 11. Object-Oriented Python: Classes, Objects, Inheritance and DataclassesIntermediate 110 min Model real things as objects. Learn classes, __init__, attributes and methods, encapsulation, inheritance, polymorphism, special (dunder) methods, class and static methods, properties and dataclasses, with a small data-pipeline example.PDF
12. 12. Files, Exceptions, JSON and CSVIntermediate 110 min Read and write text, CSV and JSON files safely with pathlib and with-blocks, handle errors with try/except/else/finally, raise your own exceptions, and log what happened, using a small "load a messy file" project.PDF
13. 13. Modules, Packages, Virtual Environments and GitIntermediate 90 min Organise code into modules and packages, install libraries with pip or uv inside virtual environments, pin dependencies, use the standard library, and version-control your projects with Git and GitHub.PDF
14. 14. Debugging, Testing, Regular Expressions and Code QualityIntermediate 100 min Find and fix bugs systematically, write automated tests with pytest, clean text with regular expressions, and keep code readable with PEP 8, type hints, linters and formatters.PDF
15. 15. NumPy: Fast Arrays, Vectorisation and BroadcastingIntermediate 120 min Learn the array library under pandas, scikit-learn and PyTorch. Create arrays, understand shape and dtype, index and slice in 1-D and 2-D, filter with boolean masks, compute with vectorised operations, aggregations along axes and broadcasting, generate random data and do basic linear algebra.PDF
16. 16. pandas I: Series, DataFrames, Reading Data, Selecting and FilteringIntermediate 120 min Meet the most-used data library. Create Series and DataFrames, read CSV and Excel files, inspect data with head, info and describe, select columns and rows with loc and iloc, filter with conditions and query, sort, add columns and save results, using the Superstore sales data.PDF
17. 17. pandas II: Data Cleaning: Missing Values, Types, Duplicates, Text and OutliersIntermediate 120 min Turn messy data into analysis-ready data. Detect and treat missing values, fix data types, remove duplicates, standardise text, parse dates, handle outliers with IQR and caps, and document every step in a reusable cleaning function.PDF
18. 18. pandas III: GroupBy, Pivot Tables, Merge/Join and Reshape (melt/unpivot)Intermediate 120 min Summarise and combine data. Use groupby with agg, transform and filter, build pivot tables and crosstabs, join tables with merge (inner, left, right, outer, anti), stack with concat, and reshape between wide and long with melt, pivot, stack and unstack.PDF
19. 19. pandas IV: Dates, Time Series, Resampling and Rolling WindowsIntermediate 110 min Work with time. Parse dates, extract parts with .dt, do date arithmetic, set a DatetimeIndex, resample to weeks, months and quarters, compute rolling and expanding windows, shifts and growth rates, and build a cohort-style monthly report with the daily sales data.PDF
20. 20. Data Visualisation I: Matplotlib and Choosing the Right ChartIntermediate 110 min Pick the right chart for the question and build it with Matplotlib's object-oriented API. Line, bar, histogram, scatter, box and pie charts, subplots, annotations, styling and saving, plus design rules that make charts honest and readable.PDF
21. 21. Data Visualisation II: Seaborn Statistical Charts and Interactive PlotlyIntermediate 100 min Make statistical charts quickly with Seaborn (distributions, categorical comparisons, relationships, correlation heatmaps, pair plots and facets) using the HR attrition data, and build interactive charts and simple dashboards with Plotly Express and Streamlit.PDF
22. 22. Exploratory Data Analysis (EDA) Case Study: Telecom Customer ChurnIntermediate 150 min Run a complete, structured EDA on 3,000 telecom customers. Frame the question, check data quality, do univariate, bivariate and segment analysis, engineer features, quantify revenue at risk and finish with findings and recommendations: the template you will reuse for every dataset and for the ML churn model.PDF
23. 23. Python and SQL: sqlite3, DuckDB, SQLAlchemy and pandas.read_sqlIntermediate 100 min Connect Python to databases. Create and query SQLite with the standard library, use parameterised queries safely, load DataFrames to tables and back, query CSV and DataFrames directly with DuckDB, connect to MySQL, PostgreSQL and SQL Server with SQLAlchemy, and decide what to do in SQL versus pandas.PDF
24. 24. APIs, JSON and Web Scraping with requests and BeautifulSoupIntermediate 100 min Collect data from the web. Understand HTTP, call REST APIs with requests (GET/POST, query parameters, headers, authentication, pagination, retries and rate limits), flatten nested JSON with json_normalize, and scrape HTML tables and pages responsibly with BeautifulSoup and pandas.read_html.PDF
25. 25. Performance and Bigger Data: Vectorisation, Memory, Parquet, Polars and DuckDBIntermediate 100 min Make Python data work fast and scale beyond memory. Measure with timeit, replace loops with vectorised code, shrink DataFrames with the right dtypes, store data as Parquet, use Polars lazy queries and DuckDB for out-of-core analytics, and know when to move to Spark and the cloud.PDF
26. 26. DSA I: Big-O, Arrays, Strings, Hashing, Two Pointers and Sliding WindowIntermediate 120 min Data structures and algorithms for data-analyst, data-scientist and data-engineer interviews. Learn Big-O time and space complexity, then the patterns that solve most array and string questions (hash maps and sets, prefix sums, two pointers, sliding window), with worked interview problems and timings.PDF
27. 27. DSA II: Stacks, Queues, Linked Lists, Recursion, Sorting and Binary SearchIntermediate 120 min Learn linear data structures (stack, queue, deque, linked list), recursion and backtracking, the main sorting algorithms with their trade-offs, and binary search including "search on the answer", with interview problems such as valid parentheses, next greater element, reverse a linked list and search in a rotated array.PDF
28. 28. DSA III: Trees, Heaps, Graphs (BFS/DFS) and Dynamic ProgrammingAdvanced 130 min Finish the DSA toolkit. Binary trees and binary search trees with traversals, heaps and priority queues with heapq (top-k, merge k lists, running median), graphs with adjacency lists, BFS and DFS, shortest paths and topological sort (as used in data pipelines), and dynamic programming from memoisation to tables.PDF
29. 29. Python Capstone: End-to-End Retail Sales Analytics ProjectAdvanced 180 min Bring the whole Python track together on the Superstore data. Structure a project, build a validated data pipeline, compute KPIs, growth and Pareto analysis, cohort retention and discount impact, create a chart pack and an Excel report, and write the README and executive summary you can put in your portfolio.PDF

Probability, CLT, hypothesis tests, the ML lifecycle, regression, classification, trees, ensembles, clustering, PCA, time series, NLP, deep learning basics, evaluation, explainability and deployment.

1. 1. Descriptive Statistics: Centre, Spread, Shape and RelationshipsBeginner 100 min Describe data with numbers before modelling it. Types of data and measurement scales, mean, median and mode, range, variance, standard deviation, IQR and coefficient of variation, percentiles and z-scores, skewness and kurtosis, covariance and correlation (Pearson and Spearman), all on the house-price and HR datasets.PDF
2. 2. Probability and Distributions: Rules, Bayes, Binomial, Poisson and NormalBeginner 110 min The language of uncertainty. Probability rules, conditional probability and Bayes' theorem (with a fraud-alert example), random variables and expected value, and the distributions analysts use most (Bernoulli, binomial, Poisson, uniform, normal, exponential), with scipy.stats and simulation.PDF
3. 3. Sampling, the Central Limit Theorem and Confidence IntervalsIntermediate 110 min Learn how a sample tells us about a population. Population vs sample, sampling methods and biases, sampling distributions and standard error, the Central Limit Theorem by simulation, confidence intervals for means and proportions (z, t and bootstrap), and how to choose a sample size.PDF
4. 4. Hypothesis Testing I: The Framework, p-values, z-tests and t-testsIntermediate 120 min Turn business questions into testable hypotheses. Null and alternative hypotheses, significance level, test statistics, p-values and critical values, one- vs two-tailed tests, Type I and II errors and power, then one-sample, two-sample (Welch) and paired t-tests and z-tests for proportions, on a delivery-time case study.PDF
5. 5. Hypothesis Testing II: Chi-Square, ANOVA, Non-Parametric Tests and Correlation TestsIntermediate 110 min Test relationships between categories with chi-square tests, compare three or more groups with one-way ANOVA and Tukey post-hoc tests, use non-parametric alternatives (Mann-Whitney, Wilcoxon, Kruskal-Wallis) when assumptions fail, test correlations, and control false discoveries when running many tests.PDF
6. 6. A/B Testing and Experimentation: Design, Power, Analysis and PitfallsIntermediate 120 min Run product experiments the way data teams at e-commerce and fintech companies do. Choose a metric and guardrails, size the test with power analysis, randomise, analyse conversion and revenue with z-tests, t-tests and bootstrap intervals, check sample-ratio mismatch, avoid peeking and novelty effects, and write the decision memo, using a new-checkout experiment.PDF
7. 7. Introduction to Machine Learning: Types, Business Framing and the ML LifecycleBeginner 100 min What machine learning is and is not, how it differs from rules and statistics, supervised vs unsupervised vs reinforcement learning, regression vs classification vs clustering, turning a business problem into an ML problem, the end-to-end ML lifecycle (CRISP-DM to MLOps), linear vs non-linear models and link functions, and where GenAI fits.PDF
8. 8. The scikit-learn Workflow: Train/Test Split, Baselines, Pipelines and Saving ModelsIntermediate 110 min Learn the standard machine-learning workflow in scikit-learn. The estimator API (fit, predict, transform), splitting data into train, validation and test sets with stratification and time order, baselines, ColumnTransformer and Pipeline to avoid leakage, comparing models, and saving and loading a trained pipeline.PDF
9. 9. Data Preprocessing and Feature Engineering for MLIntermediate 120 min Prepare features that models can learn from. Imputation strategies, outlier handling, encoding categorical variables (one-hot, ordinal, target encoding), scaling (standard, min-max, robust), transformations (log, Box-Cox/Yeo-Johnson), binning, interactions and polynomial features, date and text features, and feature selection, all inside leak-free pipelines.PDF
10. 10. Linear Regression: Fitting, Interpreting, Assumptions and DiagnosticsIntermediate 130 min Predict a number and explain it. Simple and multiple linear regression with ordinary least squares, interpreting coefficients, dummy variables and log targets, R², adjusted R², RMSE and MAE, the LINE assumptions and residual diagnostics, multicollinearity and VIF, outliers and influence, with statsmodels for inference and scikit-learn for prediction on the house-price data.PDF
11. 11. Gradient Descent: How Models Learn (Built from Scratch in NumPy)Intermediate 110 min Understand the optimisation engine behind linear regression, logistic regression and neural networks. Cost functions, gradients and the update rule, implementing batch gradient descent for linear regression in NumPy, the learning rate, feature scaling, stochastic and mini-batch variants, convergence checks, and comparison with the closed-form OLS solution and scikit-learn's SGDRegressor.PDF
12. 12. Overfitting, Polynomial Regression and Regularisation (Ridge, Lasso, Elastic Net)Intermediate 110 min See overfitting and underfitting happen, learn the bias-variance trade-off with polynomial regression, and control model complexity with L2 (Ridge), L1 (Lasso) and Elastic Net penalties, including choosing the penalty strength with cross-validation, feature selection with Lasso, and learning curves.PDF
13. 13. Logistic Regression: Probabilities, Odds Ratios and Credit-Risk ScoringIntermediate 120 min Classify with probabilities. The sigmoid and log-odds, maximum likelihood and log loss, fitting logistic regression with statsmodels (inference, odds ratios) and scikit-learn (pipelines, regularisation), thresholds and decisions, multiclass logistic regression, and scoring new applicants in the Bank Loans case study.PDF
14. 14. Classification Metrics: Confusion Matrix, Precision, Recall, ROC, PR and Business ValueIntermediate 110 min Measure classifiers properly. The confusion matrix, accuracy and why it misleads, precision, recall, specificity, F1 and F-beta, ROC curves and AUC, precision-recall curves, log loss and calibration, lift and gains charts, choosing thresholds by cost or capacity, and multiclass averaging, using the telecom churn model.PDF
15. 15. Cross-Validation and Hyperparameter Tuning: K-Fold, GridSearchCV, RandomizedSearchCV and OptunaIntermediate 110 min Estimate performance reliably and tune models without fooling yourself. Hold-out vs k-fold, stratified, grouped and time-series cross-validation, cross_validate with several metrics, GridSearchCV and RandomizedSearchCV over pipelines, nested cross-validation, Bayesian optimisation with Optuna, and the common leakage traps.PDF
16. 16. K-Nearest Neighbours: Classifier and Regressor (Bank Loans Case Study)Intermediate 100 min The simplest learning algorithm: predict from the most similar past examples. Distance metrics, choosing k, why scaling is essential, weighted voting, the curse of dimensionality, KNN for classification and regression, tuning with GridSearchCV, and the full Bank Loans case study from the live class with metrics and scoring of new applicants.PDF
17. 17. Decision Trees: Splits, Impurity, Pruning and Interpretable RulesIntermediate 110 min Learn the model behind random forests and gradient boosting. How trees split data with Gini impurity, entropy and variance reduction, growing and visualising a tree, reading it as business rules, controlling overfitting with depth, minimum leaf size and cost-complexity pruning, regression trees, feature importance and the strengths and weaknesses of single trees.PDF
18. 18. Ensembles I: Bagging and Random ForestsIntermediate 100 min Combine many models to beat one. The wisdom of crowds, bootstrap aggregating (bagging), random forests with feature subsampling, out-of-bag evaluation, the key hyperparameters, feature importance (impurity vs permutation), extra trees, voting and stacking ensembles, applied to telecom churn.PDF
19. 19. Ensembles II: Gradient Boosting, XGBoost, LightGBM and CatBoostAdvanced 120 min The algorithm family that wins most tabular-data problems. Boosting intuition (AdaBoost to gradient boosting), fitting trees to residuals by hand, learning rate and number of trees, early stopping, scikit-learn's HistGradientBoosting, XGBoost and LightGBM APIs and key hyperparameters, categorical features and CatBoost, and a tuned comparison on churn and house prices.PDF
20. 20. Support Vector Machines and Naive BayesIntermediate 100 min Two classic classifiers with very different ideas. SVMs find the widest margin between classes, use soft margins (C) and kernels (RBF, polynomial) for non-linear boundaries, and need scaling; Naive Bayes applies Bayes' theorem with an independence assumption and is a fast, strong baseline for text. Includes SVR, probability calibration and when to choose each.PDF
21. 21. Imbalanced Classification: Fraud Detection with Class Weights, Resampling and ThresholdsAdvanced 110 min Most valuable classification problems are rare-event problems (fraud, defaults, churn, failures). Learn why accuracy and naive training fail, the right metrics (PR-AUC, recall at fixed precision, cost), class weights, undersampling, oversampling and SMOTE inside pipelines, threshold moving, and anomaly-detection alternatives, on a synthetic card-fraud dataset with about 1.4% fraud.PDF
22. 22. Clustering I: K-Means and RFM Customer SegmentationIntermediate 120 min Find groups without labels. Heuristic RFM segmentation (recency, frequency, monetary scores and named segments) as taught in class, then K-means clustering, scaling and log transforms, choosing k with the elbow method and silhouette score, profiling and naming clusters, and turning segments into marketing actions, on 4,500 retail transactions.PDF
23. 23. Clustering II and Dimensionality Reduction: Hierarchical, DBSCAN, Gaussian Mixtures, PCA, t-SNE and UMAPAdvanced 120 min Go beyond K-means. Agglomerative hierarchical clustering with dendrograms and linkage choices, density-based DBSCAN for irregular shapes and outliers, Gaussian mixture models with soft assignments, and dimensionality reduction with PCA (explained variance, loadings, compression), plus t-SNE and UMAP for visualisation, applied to video-game sales and HR data.PDF
24. 24. Time-Series Forecasting: Decomposition, Baselines, ETS, ARIMA and ML with Lag FeaturesAdvanced 130 min Forecast demand properly. Components of a time series (trend, seasonality, noise), decomposition with STL, stationarity and autocorrelation (ADF, ACF/PACF), honest baselines (naive, seasonal naive, moving average), exponential smoothing (Holt-Winters), SARIMA, machine learning with lag and calendar features, backtesting with forecast metrics (MAE, RMSE, MAPE, WAPE), prediction intervals, and a guide to Prophet, statsforecast and foundation models.PDF
25. 25. NLP and Text Analytics: Cleaning, TF-IDF, Sentiment Classification, Topics and EmbeddingsAdvanced 130 min Turn customer reviews into insight. Text cleaning and tokenisation, stop words, stemming and lemmatisation, bag-of-words and TF-IDF with n-grams, a sentiment classifier with error analysis, aspect-level insight, topic modelling with NMF, document similarity, and how embeddings and LLMs change NLP, on 3,000 e-commerce reviews.PDF
26. 26. Neural Networks and Deep Learning: From a Neuron to TransformersAdvanced 130 min Understand the models behind modern AI. Neurons, weights and activation functions, forward pass and backpropagation, a two-layer network written from scratch in NumPy, multilayer perceptrons in scikit-learn on images and tabular data, training practice (learning rate, batches, epochs, early stopping, dropout), and a map of CNNs, RNNs, transformers and LLMs with PyTorch and Keras code.PDF
27. 27. Explainable AI and MLOps: SHAP, Fairness, Deployment, Monitoring and Responsible MLAdvanced 130 min Make models trustworthy and useful in production. Global and local explanations (permutation importance, partial dependence, SHAP), fairness checks across groups, packaging a pipeline, serving predictions with FastAPI, batch scoring, experiment tracking with MLflow, monitoring data drift and performance, model cards, and the regulations and responsible-AI practices that apply in India and globally.PDF
28. 28. ML Capstone: End-to-End Churn Prediction and Retention StrategyAdvanced 180 min Put the whole ML track together. Frame the churn problem, build leak-free features, compare baselines and models with cross-validation, tune the winner, choose a profit-maximising threshold under a budget, explain predictions, design segment-level retention actions with an A/B test, and write the model card, deployment plan and executive summary for your portfolio.PDF

How LLMs work, prompting, LLM APIs in Python, embeddings and vector search, RAG, tool use, agents, MCP, evaluation, guardrails, fine-tuning and responsible AI.

1. 1. How Large Language Models Work: Tokens, Embeddings, Transformers and TrainingBeginner 100 min Open the black box. Tokens and tokenisation (built from scratch), next-token prediction and sampling with temperature, embeddings, attention and the transformer, pre-training, instruction tuning and RLHF, context windows, reasoning models, why LLMs hallucinate, and how to choose among Claude, GPT, Gemini, Llama, Mistral, DeepSeek and Qwen.PDF
2. 2. Prompt Engineering in Depth: Patterns, Templates, Few-Shot, Reasoning and Structured OutputBeginner 110 min Write prompts that work reliably in products, not just in chat. The anatomy of a production prompt (role, context, task, constraints, format, examples), XML tags and delimiters, few-shot examples chosen by similarity, step-by-step reasoning and extended thinking, structured JSON output with validation, prompt templates and versioning, prompt chaining, and a simple evaluation loop.PDF
3. 3. Calling LLM APIs from Python: Messages, Streaming, Structured Output, Costs and ReliabilityIntermediate 120 min Move from the chat window to code. Set up API keys safely, send messages with system prompts, hold multi-turn conversations, stream responses, get schema-valid JSON, control reasoning effort, cache long prompts, count tokens and estimate cost, batch large jobs, handle errors and retries, and run the same pattern with other providers, with a runnable offline simulator for practice.PDF
4. 4. Embeddings and Vector Search: Meaning as Numbers, Similarity and Vector DatabasesIntermediate 110 min Learn the foundation of semantic search and RAG. What embeddings are, how word and sentence embeddings are learned (trained here from scratch on our reviews), cosine similarity, document embeddings, building a vector index, exact vs approximate nearest-neighbour search (IVF, HNSW), evaluating retrieval with recall@k, hybrid keyword + vector search, and choosing embedding models and vector databases.PDF
5. 5. Retrieval-Augmented Generation (RAG): Build, Evaluate and Improve a Q&A AssistantIntermediate 140 min Ground an LLM in your own documents. RAG architecture, document loading and chunking, indexing, hybrid retrieval and re-ranking, prompt assembly with citations, answer generation (offline simulator plus real Claude code with the citations feature), evaluation of retrieval and answers, failure modes and advanced patterns (query rewriting, contextual retrieval, metadata filters, agentic and graph RAG), built end to end on a retailer's policy handbook.PDF
6. 6. Tool Use and Function Calling: Letting LLMs Query Data, Call APIs and Take ActionsIntermediate 120 min Connect a model to the outside world. How function calling works (tool definitions, JSON schemas, tool_use and tool_result blocks), the request–execute–respond loop, writing good tool descriptions, strict schemas and validation, parallel calls, error handling, server-side tools (web search, code execution), and safety controls, with a runnable sales-analytics assistant that answers questions by calling SQL and calculator tools.PDF
7. 7. AI Agents and Agentic AI: Loops, Planning, Memory, Multi-Agent Systems and When to Use ThemIntermediate 120 min Understand what makes software "agentic". Workflows vs agents, the agent loop (observe, think, act), ReAct-style reasoning with tools, planning and task decomposition, short- and long-term memory, guardrails and stopping conditions, multi-agent patterns (orchestrator–workers, evaluator–optimiser, routing), the 2026 agent platforms, and how to decide whether a problem needs an agent at all, with a runnable data-analysis agent.PDF
8. 8. Model Context Protocol (MCP): Connecting AI Assistants to Your Data and ToolsIntermediate 100 min Learn the open standard that lets any AI application use any tool or data source. MCP architecture (hosts, clients, servers), the three primitives (tools, resources, prompts), transports (stdio and Streamable HTTP), building and testing a real MCP server for sales data in Python, connecting it to Claude Desktop, Claude Code and other clients, using remote MCP servers from the API, and securing MCP deployments.PDF
9. 9. Evaluating LLM Applications and Building Guardrails: Test Sets, LLM-as-Judge, Injection, PII and SafetyIntermediate 120 min Ship GenAI features you can trust. Build evaluation sets, choose metrics (exact match, rubric scores, groundedness), calibrate LLM-as-judge against humans, run regression tests on every prompt change, and protect applications with input and output guardrails: prompt-injection defences, PII detection and redaction (Indian identifiers), schema validation, refusal handling, red-teaming and production monitoring.PDF
10. 10. Customising Models: Prompting vs RAG vs Fine-Tuning, LoRA, Distillation and Open-Weight ModelsAdvanced 100 min Decide how to adapt a model to your task. The customisation ladder (prompting, few-shot, RAG, tools, fine-tuning, training), what fine-tuning is good and bad at, preparing training data, parameter-efficient fine-tuning with LoRA and QLoRA (with the maths), distillation from a large to a small model, running open-weight models locally with Ollama and at scale with vLLM, and the cost, privacy and quality trade-offs of API vs self-hosting.PDF
11. 11. GenAI for Data Work: Text-to-SQL, AI Coding Assistants, Data Cleaning and Automated InsightsIntermediate 120 min Apply LLMs to everyday analytics and engineering. Schema-aware text-to-SQL with guardrails and execution-accuracy evaluation, AI coding assistants and agents in notebooks and IDEs, LLM-assisted data cleaning (normalising messy categories, extracting fields from text), generating narrative insights from tables safely, and a workflow that keeps the analyst in control.PDF
12. 12. Building and Shipping a GenAI App: Architecture, Streamlit/FastAPI, Cost, Observability and Responsible AI (Capstone)Intermediate 150 min Put the GenAI track together. Reference architecture for an LLM application, a support-assistant capstone (RAG + tools + guardrails + evaluation), a Streamlit chat front end and a FastAPI back end, session memory, streaming, cost and latency budgets, caching, observability and tracing, deployment options, a launch checklist and responsible-AI governance, with a runnable cost and capacity planner.PDF

No-code track for PMs, BAs and senior PMs: APIs, MCP with Claude, how AI agents work and how to build them, connecting apps with n8n, Make, Zapier and Replit, a complete Claude Code tutorial, and voice agents and voice cloning.

1. 1. APIs Without Code: How Apps Talk, REST, JSON, Authentication, Webhooks and Postman for PMs and BAsBeginner 120 min Understand APIs well enough to write requirements, read documentation, test endpoints and design integrations without writing code. Learn requests and responses, REST verbs and status codes, JSON, authentication (API keys, OAuth 2.0), rate limits and pagination, webhooks vs polling, and how to try any API in Postman, then turn it into a PRD-ready integration spec.PDF
2. 2. MCP (Model Context Protocol) Without Code: Connect Claude to Your Apps and DataBeginner to Intermediate 120 min Learn what the Model Context Protocol is and why it became the standard way to connect AI assistants to tools and data. Understand hosts, clients, servers, tools, resources and prompts; use ready-made connectors in Claude (Google Drive, Gmail, Slack, GitHub, Notion, Jira); add local and remote MCP servers; expose your own workflows as MCP tools with n8n or Zapier without code; and manage security and permissions.PDF
3. 3. How AI Agents Work and How to Build One Without CodeIntermediate 150 min Understand the agent loop (perceive, reason, act, observe), the building blocks of an agent (model, instructions, tools, memory, knowledge, guardrails), the main design patterns, and when not to use an agent. Then build agents without code in Claude (Projects, Skills, connectors), n8n, Make, Zapier Agents and Replit Agent, and evaluate them like a product.PDF
4. 4. Connect All Your Apps with Claude, n8n, Make, Zapier and ReplitIntermediate 150 min Design integrations across your tools with Claude as the brain and n8n, Make, Zapier or Replit as the hands. Learn the four integration patterns, how to pick a platform, how Claude helps you design and debug automations, and build three end-to-end projects - a lead pipeline, a feedback-to-roadmap loop and a weekly executive report - with costs, reliability and security in mind.PDF
5. 5. Claude Code Complete Tutorial: From First Prompt to Agents, MCP, Hooks and AutomationBeginner to Advanced 180 min Learn Claude Code, Anthropic's agentic coding tool, from installation to advanced workflows. Work in the terminal, IDE, desktop and web; give context with CLAUDE.md; use plan mode, permissions and checkpoints; run slash commands, custom commands and Skills; add MCP servers; automate with hooks, subagents, headless mode and GitHub Actions; and follow best practices that work for PMs, analysts and engineers.PDF
6. 6. Voice Agents and Voice Cloning: Tools, Architecture, Build Steps and Responsible UseIntermediate 120 min Learn how AI voice agents work (speech-to-text, LLM, text-to-speech, turn-taking, telephony), compare the main platforms (ElevenLabs, Vapi, Retell AI, Bland AI, Synthflow, OpenAI Realtime, Twilio), build a no-code appointment-booking voice agent connected to your calendar and CRM, clone a voice with consent, and design for latency, Indian languages, compliance and quality.PDF

Complete tutorials for the design and automation tools used across the programs: Figma (design systems, prototypes, Dev Mode, Figma Make) and n8n (workflows, APIs, AI agents, MCP).

1. 1. Figma Complete Tutorial: Interface, Auto Layout, Components, Variables, Prototypes, Dev Mode and Figma AIBeginner to Advanced 180 min Learn Figma from zero to a handoff-ready design. Tour the interface, master frames, shapes, text and the 8-point grid, build responsive layouts with auto layout and constraints, create components with variants and properties, use styles and variables (light and dark modes), prototype flows with Smart Animate and overlays, hand off in Dev Mode, run workshops in FigJam, and generate working prototypes with Figma AI and Figma Make. Includes a dashboard-design project for data roles.PDF
2. 2. n8n Complete Tutorial: Workflow Automation, Data Pipelines and AI AgentsBeginner to Advanced 180 min Learn n8n, the open, self-hostable workflow-automation tool. Set it up on n8n Cloud or Docker, understand triggers, nodes, credentials, items and expressions, transform data with Edit Fields, IF, Switch, Merge, Loop and Code nodes, call any API with HTTP Request, handle errors and schedules, build AI agents with chat models, memory, tools and vector stores, and connect n8n to MCP. Four complete projects: lead capture, a daily sales report, an email-triage AI agent and a document Q&A bot.PDF

End-to-end capstone, portfolio and GitHub, analyst and data scientist interview preparation with model answers, and a 90-day plan.

1. 1. Portfolio, GitHub and Resume for Data RolesBeginner 90 min Turn your learning into evidence employers trust. Choose three to five portfolio projects, structure them on GitHub with strong READMEs, publish dashboards and apps, write an ATS-friendly one-page resume with quantified bullets, and optimise LinkedIn and Naukri profiles for analyst, data scientist, data engineer and GenAI roles.PDF
2. 2. Data Analyst Interview Preparation: SQL, Excel, Power BI, Statistics and Case QuestionsIntermediate 120 min Prepare for analyst interviews with the question types that appear most often, model answers and practice links: SQL coding rounds (joins, aggregation, window functions), Excel and Power BI questions, statistics and A/B testing, metric and product-sense cases, and behavioural questions with the STAR method.PDF
3. 3. Data Scientist, ML and GenAI Interview Preparation: Questions and Model AnswersAdvanced 120 min A compact question bank with model answers for data scientist, ML engineer and GenAI roles: Python and pandas, statistics, ML fundamentals, model evaluation, feature engineering, system and case design (churn, fraud, recommendations), MLOps, LLMs, RAG and agents, plus how to handle take-home assignments and live coding.PDF
4. 4. Your 90-Day Plan: Learning Roadmap, Job Search Strategy and Growing in Your First RoleBeginner 60 min Turn this handbook into a job. A week-by-week 90-day study and project plan for analyst, data science and GenAI tracks, a weekly routine, a job-search funnel with referrals and outreach messages, salary research and negotiation basics, and habits that help you grow in the first 6 months on the job.PDF

Prompt engineering is how you ask; context engineering is what the AI gets to see when it answers: your instructions, examples, documents, data, memory and tools. Getting both right is the first and most useful AI skill.

1. Basic Class 1: From prompts to contextSee why the information you give the AI matters more than clever wording.
2. Basic Class 2: Prompt patterns that workUse proven patterns: examples, step-by-step, roles, critique and iteration.
3. Intermediate Class 3: Structured outputs: JSON, tables and fixed sectionsGet predictable output other tools and people can use directly.
4. Intermediate Class 4: Task decomposition and chainingBreak big jobs into steps the AI can do reliably.
5. Intermediate Class 5: Long context: compression, summaries and memoryKeep long projects accurate when chats and documents get large.
6. Intermediate Class 6: Documents, PDFs, scans and OCRGet reliable information out of PDFs, slides, spreadsheets and images.
7. Advanced Class 7: Source attribution, fact-checking and knowledge graphsMake AI outputs traceable to evidence and organise knowledge by relationships.
8. Advanced Class 8: Context engineering for products and agentsDesign what your AI feature or agent sees, and test it.

An eval is a repeatable test that measures how well an AI feature does its job on a fixed set of examples, with a clear way to score each answer. It's the quality bar, the launch gate and the product spec for AI.

1. Basic Class 1: What an eval is, and why PMs own itUnderstand evals as the unit test, the quality bar and the product spec for AI features.
2. Basic Class 2: Defining success: criteria, rubrics and failure modesTurn a fuzzy goal ('helpful answers') into criteria anyone can score the same way.
3. Basic Class 3: Building the eval dataset (golden set)Create a test set that represents real users, including the hard and risky cases.
4. Intermediate Class 4: Grading methods: code, humans and LLM-as-judgeChoose the cheapest grader that measures what matters, and know when to use humans.
5. Intermediate Class 5: Error analysis: finding what actually breaksRead outputs systematically, name the failure modes and fix the biggest ones first.
6. Intermediate Class 6: Evaluating RAG (retrieval-augmented generation)Measure retrieval and generation separately, so you know which half to fix.
7. Advanced Class 7: Evaluating agents and tool useMeasure multi-step AI agents on outcomes, the path they took, cost and safety.
8. Advanced Class 8: Safety, responsible AI and red-teamingTest for harm, privacy leaks, bias and jailbreaks before attackers and journalists do.
9. Advanced Class 9: Online evals: A/B tests, monitoring and regression gatesConnect offline scores to real user value and catch quality drops in production.
10. Advanced Class 10: Eval ops: tools, cost, the AI PRD and your capstoneRun evals as a product practice: tooling, budgets, documentation and team rituals.

Webhooks tell your system the moment something happens; triggers decide when automations run; browser automation does the clicking when there's no API; and the Playwright MCP lets an AI assistant like Claude use a real browser for you. Together they're the most-used web automation stack in 2026.

1. Basic Class 1: The web-automation stack: APIs, webhooks, triggers, browsersSee how the pieces fit, and pick the right one for each job.
2. Basic Class 2: Webhooks explained (with real examples)Understand webhooks and receive your first one.
3. Intermediate Class 3: Building reliable webhook receiversVerify, process and retry webhooks safely in production.
4. Basic Class 4: Triggers: schedules, events, polling and manualChoose how and when automations start.
5. Intermediate Class 5: Playwright for browser automationAutomate any website reliably with Playwright.
6. Intermediate Class 6: Playwright MCP: let Claude and other AI assistants use a browserInstall and use the Playwright MCP server with Claude Code, Claude Desktop, Cursor and VS Code.
7. Intermediate Class 7: Other browser-automation frameworks and MCPsKnow the alternatives and when each wins.
8. Advanced Class 8: AI browser agents and cloud browsersUse AI-native automation tools and hosted browsers at scale.
9. Advanced Class 9: Workflow platforms: n8n, Zapier, Make, Pipedream, RPA and scrapersRun webhooks, triggers and browser steps as complete workflows.
10. Advanced Class 10: Production: security, legality, monitoring, evals and capstoneRun web automations safely and prove they work.

Orchestration is how AI steps, tools and people are connected into a workflow that runs reliably; monitoring is how you know it keeps working. Start with a simple workflow and add agents only where they are needed.

1. Basic Class 1: Workflows vs agents: start simpleKnow when a fixed workflow is enough and when an agent is worth it.
2. Basic Class 2: Core patterns: chaining, routing and parallelisationUse the three most common orchestration patterns.
3. Intermediate Class 3: Orchestrator-workers and evaluator-optimizerUse the two most powerful agentic patterns safely.
4. Intermediate Class 4: State, checkpoints and long-running workMake workflows resumable and traceable.
5. Intermediate Class 5: Human-in-the-loop, approvals and error recoveryPut people at the right points and recover from failures safely.
6. Advanced Class 6: Multi-agent systemsKnow when splitting work across agents helps, and how to coordinate them.
7. Advanced Class 7: Triggers, scheduling and cost controlRun workflows automatically and within budget.
8. Advanced Class 8: Monitoring, observability and the final projectKnow when a workflow breaks or degrades, and fix it fast.

AI builder tools let PMs and non-coders research, write, prototype, build websites and apps, and connect tools together. This course shows what each tool is best at and walks you through real builds step by step.

1. Basic Class 1: Choosing the right AI toolKnow the four kinds of AI tools and when to use each.
2. Basic Class 2: Claude for PMs: what you can do and howUse Claude's Projects, Artifacts, research, connectors and Skills in daily PM work.
3. Basic Class 3: ChatGPT for PMs: Projects, GPTs, Canvas, research and agentUse ChatGPT's main features for research, documents, data and automation.
4. Basic Class 4: Gemini for PMs: Gemini app, Gems, Deep Research, NotebookLM, Workspace, AI StudioUse Google's AI tools, especially if your team lives in Google Workspace.
5. Intermediate Class 5: Lovable: from idea to a working app (no code)Build and publish an app with Lovable, including a database, login and your own domain.
6. Intermediate Class 6: Claude Code and OpenAI Codex: coding agents for PMsUse coding agents to build and change real websites and apps, safely.
7. Intermediate Class 7: Tutorial: create a website, add pages and publish on your domainFollow one complete path from idea to a live site you own.
8. Intermediate Class 8: Connect your tools: Zapier, Make, n8n, webhooks and MCPMake your website and tools talk to each other.
9. Advanced Class 9: Connect other websites and servicesEmbed, integrate and call other websites safely.
10. Advanced Class 10: PM workflows end to end and capstoneCombine tools into a real product workflow.

Before LLMs, most AI in products was machine learning that predicts, classifies, ranks or recommends (fraud scores, feeds, ETAs). PMs still own these products: the problem, the data, the metric trade-offs and the lifecycle.

1. Basic Class 1: ML problem typesRecognise which ML problem you're solving.
2. Basic Class 2: Data and labellingGet the data an ML product needs.
3. Intermediate Class 3: Metrics and trade-offsChoose model metrics that match business costs.
4. Intermediate Class 4: Recommendations and personalisationUnderstand how recommenders work and are measured.
5. Advanced Class 5: Model lifecycle and MLOpsKeep models working after launch.
6. Advanced Class 6: Working with data scientists and final projectCollaborate well and ship an ML feature.

AI governance is the set of policies, reviews and controls that make sure AI is safe, fair, legal and accountable, so teams can ship faster with fewer surprises.

1. Basic Class 1: Principles and risksKnow the main responsible-AI principles and risks.
2. Basic Class 2: Regulations to knowUnderstand the main laws and frameworks at a PM level.
3. Intermediate Class 3: AI risk and impact assessmentAssess a feature before launch.
4. Intermediate Class 4: Policies and documentationWrite the documents governance needs.
5. Advanced Class 5: Operating governanceRun governance without slowing teams down.
6. Advanced Class 6: Final projectGovern a real AI feature.

A second brain is a personal knowledge system outside your head: notes, documents, meetings and ideas stored in one trusted place. With AI, it becomes something you can talk to: ask questions across everything you've saved and get answers with sources.

1. Basic Class 1: What a second brain is, and why AI changes itUnderstand the idea, the PARA and CODE methods, and what AI adds.
2. Basic Class 2: Capture: getting things into the systemSet up fast, low-friction capture from phone, browser, reading and voice.
3. Basic Class 3: Organise and distilTurn a pile of captures into findable, useful notes.
4. Intermediate Class 4: Ask AI across your notesUse NotebookLM, Claude and ChatGPT Projects, Notion AI and Obsidian plugins to question your knowledge.
5. Intermediate Class 5: Context engineering and AI memoryGive AI the right context about you and your work, and manage what it remembers.
6. Intermediate Class 6: Pipelines: meetings, email, reading and learningAutomatically feed your second brain from meetings, email and courses.
7. Advanced Class 7: Second brains for PMs, students and teamsApply the system to product work, exam prep and team knowledge.
8. Advanced Class 8: Build your own: MCP, automation and evaluationConnect AI assistants to your notes with MCP and automation, and make sure answers stay accurate.

An AI engineer builds, ships and runs AI-powered software: calling and customising models, connecting them to data and tools, testing quality, deploying reliably, keeping cost, safety and security under control, and improving the system with real usage.

1. Basic Class 1: The AI engineer role and the lifecycleKnow what the job is, how it differs from neighbouring roles, and the lifecycle you'll work in.
2. Basic Class 2: Foundations: Python, software engineering, Git, APIs, LinuxBuild the engineering base that every AI system sits on.
3. Basic Class 3: Math, statistics and machine-learning essentialsKnow enough ML to choose methods, read metrics and debug models.
4. Intermediate Class 4: LLM engineering: model APIs, prompts, structured output, cost and latencyBuild reliable features on top of model APIs.
5. Intermediate Class 5: RAG, embeddings and vector databasesBuild search and question-answering over private data, and measure it.
6. Intermediate Class 6: Agents, tool use and MCPBuild agents that call tools, with control and observability.
7. Advanced Class 7: Customising models: fine-tuning, distillation and open-source modelsDecide when to fine-tune, and how, and when not to.
8. Advanced Class 8: Evals, observability, guardrails and red-teamingMake quality measurable and keep it that way.
9. Advanced Class 9: Deployment and LLMOps: APIs, containers, cloud, CI/CD, scaling and costShip AI services that are reliable, fast and affordable.
10. Advanced Class 10: Data engineering for AI, security and governanceFeed AI systems good data and keep them safe and compliant.
11. Advanced Class 11: System design for AI products and working with the teamDesign end-to-end AI systems and collaborate across roles.
12. Advanced Class 12: Career: roadmap, portfolio projects, interviews and job descriptionsPlan 12 months, build proof, and prepare for hiring loops.

Relational databases, ER diagrams and keys, DDL/DML, filtering, aggregation, joins, set operators, subqueries, window functions, CTEs, views, procedures and performance.

1. 1. Databases, RDBMS & SQL BasicsBeginner 100 min Understand what a database is, how relational databases organise data into tables, the vocabulary of SQL, the four families of SQL statements (DDL, DML, DCL, TCL), how SQL compares with Excel, and how to install SQL Server and SSMS and run your first query.PDF
2. 2. ER Diagrams, Relationships, Keys & NormalisationBeginner 110 min Design databases the way professionals do. Draw ER diagrams, read crow's-foot notation, model one-to-one, one-to-many and many-to-many relationships, master every type of key (super, candidate, primary, alternate, composite, foreign, surrogate) and normalise tables to 3NF.PDF
3. 3. Data Types, Operators, NULLs, Variables & CommentsBeginner 100 min Choose the right SQL Server data type for every column, use arithmetic, comparison, logical and string operators correctly, understand NULL and three-valued logic, declare variables, follow naming rules and write readable, commented SQL.PDF
4. 4. DDL, DML & Constraints: CREATE, ALTER, INSERT, UPDATE, DELETEBeginner 120 min Create and change tables with DDL (CREATE, ALTER, DROP, TRUNCATE), add, change and remove rows with DML (INSERT, UPDATE, DELETE, MERGE), protect data quality with constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT, IDENTITY) and understand DELETE vs TRUNCATE vs DROP.PDF
5. 5. SELECT, WHERE, ORDER BY & Filtering OperatorsBeginner 120 min Retrieve exactly the rows and columns you need. Master SELECT, aliases with AS, DISTINCT, TOP, WHERE with comparison and logical operators, IN, BETWEEN, LIKE wildcards, IS NULL, ORDER BY, and the logical order in which SQL processes a query.PDF
6. 6. SQL Functions: Aggregate, Text, Date & NumberBeginner–Intermediate 120 min Use SQL Server's built-in functions to summarise, clean and reshape data. Aggregate functions (COUNT, SUM, AVG, MIN, MAX, STRING_AGG), text functions (LEN, LEFT, RIGHT, SUBSTRING, CHARINDEX, REPLACE, TRIM, UPPER, CONCAT), date functions (GETDATE, YEAR, MONTH, DATEPART, DATENAME, DATEADD, DATEDIFF, EOMONTH) and number functions (ROUND, CEILING, FLOOR, ABS, POWER, SQRT), plus CAST and CONVERT.PDF
7. 7. GROUP BY, HAVING, CASE WHEN, ROLLUP, CUBE & COALESCEIntermediate 130 min Turn rows into business summaries. Group with GROUP BY, filter groups with HAVING, create conditional columns and buckets with CASE WHEN, pivot data with conditional aggregation, and add subtotals and grand totals with ROLLUP, CUBE and GROUPING SETS, cleaning the labels with COALESCE and GROUPING().PDF
8. 8. Joins and a Retail Case StudyIntermediate 150 min Combine tables with INNER, LEFT, RIGHT, FULL OUTER, CROSS and SELF joins, find missing matches with anti-joins, avoid the classic join mistakes (row explosion, filters in ON vs WHERE), and answer a full set of business questions on the retail database.PDF
9. 9. Set Operators: UNION, UNION ALL, INTERSECT & EXCEPTIntermediate 80 min Stack and compare query results. Append data with UNION ALL, remove duplicates with UNION, find common rows with INTERSECT and differences with EXCEPT, follow the rules for compatible columns, and know when to use a set operator instead of a join.PDF
10. 10. Subqueries, Derived Tables & EXISTSIntermediate 120 min Put queries inside queries. Use scalar subqueries, multi-row subqueries with IN, ANY and ALL, correlated subqueries, EXISTS and NOT EXISTS, and derived tables (inline views) to answer questions that one level of SELECT cannot, and learn when a join, CTE or window function is better.PDF
11. 11. OFFSET-FETCH and Window Functions I: OVER, PARTITION BY & RankingIntermediate–Advanced 130 min Page through results with OFFSET and FETCH, then learn window functions, the most powerful analytics feature in SQL. Understand OVER and PARTITION BY, rank rows with ROW_NUMBER, RANK, DENSE_RANK and NTILE, solve top-N-per-group and de-duplication problems, and compare aggregate windows with GROUP BY.PDF
12. 12. Window Functions II: Running Totals, LEAD, LAG & FramesAdvanced 130 min Build time-series analytics in SQL. Use window frames (ROWS BETWEEN) for running totals and moving averages, LAG and LEAD for month-on-month and year-on-year growth, FIRST_VALUE and LAST_VALUE, cumulative percentages, PERCENT_RANK, CUME_DIST and PERCENTILE_CONT, and gaps-and-islands patterns.PDF
13. 13. CTEs, Views, Temporary Tables, Stored Procedures & FunctionsAdvanced 130 min Write queries other people can read and reuse. Use CTEs (including recursive CTEs for hierarchies and calendars), create views for reusable logic and security, use temp tables and table variables, and build stored procedures, user-defined functions, parameters and basic error handling in T-SQL.PDF
14. 14. Transactions, Indexes, Performance & SecurityAdvanced 110 min Make SQL safe and fast. Understand transactions and ACID (BEGIN, COMMIT, ROLLBACK, SAVE), isolation and locking, indexes (clustered, non-clustered, covering), how to read an execution plan, a checklist for writing fast queries, and permissions with GRANT, REVOKE and roles (DCL).PDF
15. 15. End-to-End SQL Case Study & Interview PracticeAdvanced 180 min Put everything together. Work through a complete analysis of the retail business (data quality, KPIs, trends, customer segmentation, retention, product and discount analysis) using joins, CTEs and window functions, then practise 25 classic SQL interview questions with model answers.PDF

Python from zero: data types, control flow, data structures, functions, OOP, files, NumPy, pandas, visualisation, dates, SQL connections, EDA and DSA for data roles.

1. 1. Introduction to Python: Why, Install & Jupyter NotebooksBeginner 90 min Learn what programming and Python are, how Python compares with other languages, how code runs (interpreters, compilers, bytecode), the properties of Python, and set up your environment (Anaconda, VS Code, Jupyter, Google Colab). Write and run your first cells, with markdown, magic commands and good notebook habits.PDF
2. 2. Variables, Data Types, Type Conversion, print(), f-strings & input()Beginner 110 min Store and name values with variables, meet Python's core data types (int, float, str, bool, None, complex), convert between types, format output with print(), f-strings and .format(), and read user input with input() and sys.argv.PDF
3. 3. Operators, Strings, Indexing, Slicing & String MethodsBeginner 120 min Master Python's operators (arithmetic, comparison, logical, assignment, membership, identity, bitwise) and work with text: string indexing and slicing, immutability, every string method an analyst needs (split, join, strip, replace, find, startswith, case methods, isdigit…), escape sequences, and cleaning real messy text.PDF
4. 4. Control Flow: if, elif, else, Nested Conditions & matchBeginner 90 min Make programs decide. Learn indentation, if/elif/else, comparison and logical conditions, truthiness, conditional expressions (ternary), nested decisions, the match statement, and how Python's if maps to Excel's IF/IFS and SQL's CASE WHEN, with data-flavoured examples.PDF
5. 5. Loops: while, for, range, break, continue & PatternsBeginner 110 min Repeat work without repeating code. Learn while and for loops, range(), enumerate(), zip(), break, continue, the loop else clause, nested loops, accumulation, search and counting patterns, and common loop bugs; with an eye on when to replace loops with vectorised pandas/NumPy.PDF
6. 6. Lists: Creating, Slicing, Methods, Sorting & ComprehensionsBeginner 120 min Store and process ordered collections with lists. Index and slice them, add, remove and sort items, understand mutability, aliasing and copying (shallow vs deep), use nested lists, list comprehensions, and common patterns for cleaning and summarising data.PDF
7. 7. Tuples & Sets: Immutable Records, Unpacking and Set AlgebraBeginner 90 min Use tuples for fixed records and multiple return values, master unpacking and named tuples, and use sets for uniqueness, fast membership tests and set algebra (union, intersection, difference) on real customer data.PDF
8. 8. Dictionaries: Key-Value Data, Counting and GroupingBeginner 100 min Store and look up data by key. Create and update dictionaries, use get, setdefault, items, Counter and defaultdict, nest dictionaries, and convert between dictionaries, JSON-like records and DataFrames using the Superstore data.PDF
9. 9. Functions: Reusable Code, Arguments, Scope and DocstringsBeginner 100 min Package logic into reusable functions. Learn def, parameters and arguments (positional, keyword, default, *args, **kwargs), return values, scope, docstrings and type hints, plus testing small functions on real data.PDF
10. 10. Comprehensions, Lambda, map/filter and GeneratorsBeginner 100 min Write compact, readable Python. Learn list, set and dict comprehensions, lambda with sorted/min/max, map and filter, any and all, generators and itertools, and when plain loops or pandas are the better choice.PDF
11. 11. Object-Oriented Python: Classes, Objects, Inheritance and DataclassesIntermediate 110 min Model real things as objects. Learn classes, __init__, attributes and methods, encapsulation, inheritance, polymorphism, special (dunder) methods, class and static methods, properties and dataclasses, with a small data-pipeline example.PDF
12. 12. Files, Exceptions, JSON and CSVIntermediate 110 min Read and write text, CSV and JSON files safely with pathlib and with-blocks, handle errors with try/except/else/finally, raise your own exceptions, and log what happened, using a small "load a messy file" project.PDF
13. 13. Modules, Packages, Virtual Environments and GitIntermediate 90 min Organise code into modules and packages, install libraries with pip or uv inside virtual environments, pin dependencies, use the standard library, and version-control your projects with Git and GitHub.PDF
14. 14. Debugging, Testing, Regular Expressions and Code QualityIntermediate 100 min Find and fix bugs systematically, write automated tests with pytest, clean text with regular expressions, and keep code readable with PEP 8, type hints, linters and formatters.PDF
15. 15. NumPy: Fast Arrays, Vectorisation and BroadcastingIntermediate 120 min Learn the array library under pandas, scikit-learn and PyTorch. Create arrays, understand shape and dtype, index and slice in 1-D and 2-D, filter with boolean masks, compute with vectorised operations, aggregations along axes and broadcasting, generate random data and do basic linear algebra.PDF
16. 16. pandas I: Series, DataFrames, Reading Data, Selecting and FilteringIntermediate 120 min Meet the most-used data library. Create Series and DataFrames, read CSV and Excel files, inspect data with head, info and describe, select columns and rows with loc and iloc, filter with conditions and query, sort, add columns and save results, using the Superstore sales data.PDF
17. 17. pandas II: Data Cleaning: Missing Values, Types, Duplicates, Text and OutliersIntermediate 120 min Turn messy data into analysis-ready data. Detect and treat missing values, fix data types, remove duplicates, standardise text, parse dates, handle outliers with IQR and caps, and document every step in a reusable cleaning function.PDF
18. 18. pandas III: GroupBy, Pivot Tables, Merge/Join and Reshape (melt/unpivot)Intermediate 120 min Summarise and combine data. Use groupby with agg, transform and filter, build pivot tables and crosstabs, join tables with merge (inner, left, right, outer, anti), stack with concat, and reshape between wide and long with melt, pivot, stack and unstack.PDF
19. 19. pandas IV: Dates, Time Series, Resampling and Rolling WindowsIntermediate 110 min Work with time. Parse dates, extract parts with .dt, do date arithmetic, set a DatetimeIndex, resample to weeks, months and quarters, compute rolling and expanding windows, shifts and growth rates, and build a cohort-style monthly report with the daily sales data.PDF
20. 20. Data Visualisation I: Matplotlib and Choosing the Right ChartIntermediate 110 min Pick the right chart for the question and build it with Matplotlib's object-oriented API. Line, bar, histogram, scatter, box and pie charts, subplots, annotations, styling and saving, plus design rules that make charts honest and readable.PDF
21. 21. Data Visualisation II: Seaborn Statistical Charts and Interactive PlotlyIntermediate 100 min Make statistical charts quickly with Seaborn (distributions, categorical comparisons, relationships, correlation heatmaps, pair plots and facets) using the HR attrition data, and build interactive charts and simple dashboards with Plotly Express and Streamlit.PDF
22. 22. Exploratory Data Analysis (EDA) Case Study: Telecom Customer ChurnIntermediate 150 min Run a complete, structured EDA on 3,000 telecom customers. Frame the question, check data quality, do univariate, bivariate and segment analysis, engineer features, quantify revenue at risk and finish with findings and recommendations: the template you will reuse for every dataset and for the ML churn model.PDF
23. 23. Python and SQL: sqlite3, DuckDB, SQLAlchemy and pandas.read_sqlIntermediate 100 min Connect Python to databases. Create and query SQLite with the standard library, use parameterised queries safely, load DataFrames to tables and back, query CSV and DataFrames directly with DuckDB, connect to MySQL, PostgreSQL and SQL Server with SQLAlchemy, and decide what to do in SQL versus pandas.PDF
24. 24. APIs, JSON and Web Scraping with requests and BeautifulSoupIntermediate 100 min Collect data from the web. Understand HTTP, call REST APIs with requests (GET/POST, query parameters, headers, authentication, pagination, retries and rate limits), flatten nested JSON with json_normalize, and scrape HTML tables and pages responsibly with BeautifulSoup and pandas.read_html.PDF
25. 25. Performance and Bigger Data: Vectorisation, Memory, Parquet, Polars and DuckDBIntermediate 100 min Make Python data work fast and scale beyond memory. Measure with timeit, replace loops with vectorised code, shrink DataFrames with the right dtypes, store data as Parquet, use Polars lazy queries and DuckDB for out-of-core analytics, and know when to move to Spark and the cloud.PDF
26. 26. DSA I: Big-O, Arrays, Strings, Hashing, Two Pointers and Sliding WindowIntermediate 120 min Data structures and algorithms for data-analyst, data-scientist and data-engineer interviews. Learn Big-O time and space complexity, then the patterns that solve most array and string questions (hash maps and sets, prefix sums, two pointers, sliding window), with worked interview problems and timings.PDF
27. 27. DSA II: Stacks, Queues, Linked Lists, Recursion, Sorting and Binary SearchIntermediate 120 min Learn linear data structures (stack, queue, deque, linked list), recursion and backtracking, the main sorting algorithms with their trade-offs, and binary search including "search on the answer", with interview problems such as valid parentheses, next greater element, reverse a linked list and search in a rotated array.PDF
28. 28. DSA III: Trees, Heaps, Graphs (BFS/DFS) and Dynamic ProgrammingAdvanced 130 min Finish the DSA toolkit. Binary trees and binary search trees with traversals, heaps and priority queues with heapq (top-k, merge k lists, running median), graphs with adjacency lists, BFS and DFS, shortest paths and topological sort (as used in data pipelines), and dynamic programming from memoisation to tables.PDF
29. 29. Python Capstone: End-to-End Retail Sales Analytics ProjectAdvanced 180 min Bring the whole Python track together on the Superstore data. Structure a project, build a validated data pipeline, compute KPIs, growth and Pareto analysis, cohort retention and discount impact, create a chart pack and an Excel report, and write the README and executive summary you can put in your portfolio.PDF

Probability, CLT, hypothesis tests, the ML lifecycle, regression, classification, trees, ensembles, clustering, PCA, time series, NLP, deep learning basics, evaluation, explainability and deployment.

1. 1. Descriptive Statistics: Centre, Spread, Shape and RelationshipsBeginner 100 min Describe data with numbers before modelling it. Types of data and measurement scales, mean, median and mode, range, variance, standard deviation, IQR and coefficient of variation, percentiles and z-scores, skewness and kurtosis, covariance and correlation (Pearson and Spearman), all on the house-price and HR datasets.PDF
2. 2. Probability and Distributions: Rules, Bayes, Binomial, Poisson and NormalBeginner 110 min The language of uncertainty. Probability rules, conditional probability and Bayes' theorem (with a fraud-alert example), random variables and expected value, and the distributions analysts use most (Bernoulli, binomial, Poisson, uniform, normal, exponential), with scipy.stats and simulation.PDF
3. 3. Sampling, the Central Limit Theorem and Confidence IntervalsIntermediate 110 min Learn how a sample tells us about a population. Population vs sample, sampling methods and biases, sampling distributions and standard error, the Central Limit Theorem by simulation, confidence intervals for means and proportions (z, t and bootstrap), and how to choose a sample size.PDF
4. 4. Hypothesis Testing I: The Framework, p-values, z-tests and t-testsIntermediate 120 min Turn business questions into testable hypotheses. Null and alternative hypotheses, significance level, test statistics, p-values and critical values, one- vs two-tailed tests, Type I and II errors and power, then one-sample, two-sample (Welch) and paired t-tests and z-tests for proportions, on a delivery-time case study.PDF
5. 5. Hypothesis Testing II: Chi-Square, ANOVA, Non-Parametric Tests and Correlation TestsIntermediate 110 min Test relationships between categories with chi-square tests, compare three or more groups with one-way ANOVA and Tukey post-hoc tests, use non-parametric alternatives (Mann-Whitney, Wilcoxon, Kruskal-Wallis) when assumptions fail, test correlations, and control false discoveries when running many tests.PDF
6. 6. A/B Testing and Experimentation: Design, Power, Analysis and PitfallsIntermediate 120 min Run product experiments the way data teams at e-commerce and fintech companies do. Choose a metric and guardrails, size the test with power analysis, randomise, analyse conversion and revenue with z-tests, t-tests and bootstrap intervals, check sample-ratio mismatch, avoid peeking and novelty effects, and write the decision memo, using a new-checkout experiment.PDF
7. 7. Introduction to Machine Learning: Types, Business Framing and the ML LifecycleBeginner 100 min What machine learning is and is not, how it differs from rules and statistics, supervised vs unsupervised vs reinforcement learning, regression vs classification vs clustering, turning a business problem into an ML problem, the end-to-end ML lifecycle (CRISP-DM to MLOps), linear vs non-linear models and link functions, and where GenAI fits.PDF
8. 8. The scikit-learn Workflow: Train/Test Split, Baselines, Pipelines and Saving ModelsIntermediate 110 min Learn the standard machine-learning workflow in scikit-learn. The estimator API (fit, predict, transform), splitting data into train, validation and test sets with stratification and time order, baselines, ColumnTransformer and Pipeline to avoid leakage, comparing models, and saving and loading a trained pipeline.PDF
9. 9. Data Preprocessing and Feature Engineering for MLIntermediate 120 min Prepare features that models can learn from. Imputation strategies, outlier handling, encoding categorical variables (one-hot, ordinal, target encoding), scaling (standard, min-max, robust), transformations (log, Box-Cox/Yeo-Johnson), binning, interactions and polynomial features, date and text features, and feature selection, all inside leak-free pipelines.PDF
10. 10. Linear Regression: Fitting, Interpreting, Assumptions and DiagnosticsIntermediate 130 min Predict a number and explain it. Simple and multiple linear regression with ordinary least squares, interpreting coefficients, dummy variables and log targets, R², adjusted R², RMSE and MAE, the LINE assumptions and residual diagnostics, multicollinearity and VIF, outliers and influence, with statsmodels for inference and scikit-learn for prediction on the house-price data.PDF
11. 11. Gradient Descent: How Models Learn (Built from Scratch in NumPy)Intermediate 110 min Understand the optimisation engine behind linear regression, logistic regression and neural networks. Cost functions, gradients and the update rule, implementing batch gradient descent for linear regression in NumPy, the learning rate, feature scaling, stochastic and mini-batch variants, convergence checks, and comparison with the closed-form OLS solution and scikit-learn's SGDRegressor.PDF
12. 12. Overfitting, Polynomial Regression and Regularisation (Ridge, Lasso, Elastic Net)Intermediate 110 min See overfitting and underfitting happen, learn the bias-variance trade-off with polynomial regression, and control model complexity with L2 (Ridge), L1 (Lasso) and Elastic Net penalties, including choosing the penalty strength with cross-validation, feature selection with Lasso, and learning curves.PDF
13. 13. Logistic Regression: Probabilities, Odds Ratios and Credit-Risk ScoringIntermediate 120 min Classify with probabilities. The sigmoid and log-odds, maximum likelihood and log loss, fitting logistic regression with statsmodels (inference, odds ratios) and scikit-learn (pipelines, regularisation), thresholds and decisions, multiclass logistic regression, and scoring new applicants in the Bank Loans case study.PDF
14. 14. Classification Metrics: Confusion Matrix, Precision, Recall, ROC, PR and Business ValueIntermediate 110 min Measure classifiers properly. The confusion matrix, accuracy and why it misleads, precision, recall, specificity, F1 and F-beta, ROC curves and AUC, precision-recall curves, log loss and calibration, lift and gains charts, choosing thresholds by cost or capacity, and multiclass averaging, using the telecom churn model.PDF
15. 15. Cross-Validation and Hyperparameter Tuning: K-Fold, GridSearchCV, RandomizedSearchCV and OptunaIntermediate 110 min Estimate performance reliably and tune models without fooling yourself. Hold-out vs k-fold, stratified, grouped and time-series cross-validation, cross_validate with several metrics, GridSearchCV and RandomizedSearchCV over pipelines, nested cross-validation, Bayesian optimisation with Optuna, and the common leakage traps.PDF
16. 16. K-Nearest Neighbours: Classifier and Regressor (Bank Loans Case Study)Intermediate 100 min The simplest learning algorithm: predict from the most similar past examples. Distance metrics, choosing k, why scaling is essential, weighted voting, the curse of dimensionality, KNN for classification and regression, tuning with GridSearchCV, and the full Bank Loans case study from the live class with metrics and scoring of new applicants.PDF
17. 17. Decision Trees: Splits, Impurity, Pruning and Interpretable RulesIntermediate 110 min Learn the model behind random forests and gradient boosting. How trees split data with Gini impurity, entropy and variance reduction, growing and visualising a tree, reading it as business rules, controlling overfitting with depth, minimum leaf size and cost-complexity pruning, regression trees, feature importance and the strengths and weaknesses of single trees.PDF
18. 18. Ensembles I: Bagging and Random ForestsIntermediate 100 min Combine many models to beat one. The wisdom of crowds, bootstrap aggregating (bagging), random forests with feature subsampling, out-of-bag evaluation, the key hyperparameters, feature importance (impurity vs permutation), extra trees, voting and stacking ensembles, applied to telecom churn.PDF
19. 19. Ensembles II: Gradient Boosting, XGBoost, LightGBM and CatBoostAdvanced 120 min The algorithm family that wins most tabular-data problems. Boosting intuition (AdaBoost to gradient boosting), fitting trees to residuals by hand, learning rate and number of trees, early stopping, scikit-learn's HistGradientBoosting, XGBoost and LightGBM APIs and key hyperparameters, categorical features and CatBoost, and a tuned comparison on churn and house prices.PDF
20. 20. Support Vector Machines and Naive BayesIntermediate 100 min Two classic classifiers with very different ideas. SVMs find the widest margin between classes, use soft margins (C) and kernels (RBF, polynomial) for non-linear boundaries, and need scaling; Naive Bayes applies Bayes' theorem with an independence assumption and is a fast, strong baseline for text. Includes SVR, probability calibration and when to choose each.PDF
21. 21. Imbalanced Classification: Fraud Detection with Class Weights, Resampling and ThresholdsAdvanced 110 min Most valuable classification problems are rare-event problems (fraud, defaults, churn, failures). Learn why accuracy and naive training fail, the right metrics (PR-AUC, recall at fixed precision, cost), class weights, undersampling, oversampling and SMOTE inside pipelines, threshold moving, and anomaly-detection alternatives, on a synthetic card-fraud dataset with about 1.4% fraud.PDF
22. 22. Clustering I: K-Means and RFM Customer SegmentationIntermediate 120 min Find groups without labels. Heuristic RFM segmentation (recency, frequency, monetary scores and named segments) as taught in class, then K-means clustering, scaling and log transforms, choosing k with the elbow method and silhouette score, profiling and naming clusters, and turning segments into marketing actions, on 4,500 retail transactions.PDF
23. 23. Clustering II and Dimensionality Reduction: Hierarchical, DBSCAN, Gaussian Mixtures, PCA, t-SNE and UMAPAdvanced 120 min Go beyond K-means. Agglomerative hierarchical clustering with dendrograms and linkage choices, density-based DBSCAN for irregular shapes and outliers, Gaussian mixture models with soft assignments, and dimensionality reduction with PCA (explained variance, loadings, compression), plus t-SNE and UMAP for visualisation, applied to video-game sales and HR data.PDF
24. 24. Time-Series Forecasting: Decomposition, Baselines, ETS, ARIMA and ML with Lag FeaturesAdvanced 130 min Forecast demand properly. Components of a time series (trend, seasonality, noise), decomposition with STL, stationarity and autocorrelation (ADF, ACF/PACF), honest baselines (naive, seasonal naive, moving average), exponential smoothing (Holt-Winters), SARIMA, machine learning with lag and calendar features, backtesting with forecast metrics (MAE, RMSE, MAPE, WAPE), prediction intervals, and a guide to Prophet, statsforecast and foundation models.PDF
25. 25. NLP and Text Analytics: Cleaning, TF-IDF, Sentiment Classification, Topics and EmbeddingsAdvanced 130 min Turn customer reviews into insight. Text cleaning and tokenisation, stop words, stemming and lemmatisation, bag-of-words and TF-IDF with n-grams, a sentiment classifier with error analysis, aspect-level insight, topic modelling with NMF, document similarity, and how embeddings and LLMs change NLP, on 3,000 e-commerce reviews.PDF
26. 26. Neural Networks and Deep Learning: From a Neuron to TransformersAdvanced 130 min Understand the models behind modern AI. Neurons, weights and activation functions, forward pass and backpropagation, a two-layer network written from scratch in NumPy, multilayer perceptrons in scikit-learn on images and tabular data, training practice (learning rate, batches, epochs, early stopping, dropout), and a map of CNNs, RNNs, transformers and LLMs with PyTorch and Keras code.PDF
27. 27. Explainable AI and MLOps: SHAP, Fairness, Deployment, Monitoring and Responsible MLAdvanced 130 min Make models trustworthy and useful in production. Global and local explanations (permutation importance, partial dependence, SHAP), fairness checks across groups, packaging a pipeline, serving predictions with FastAPI, batch scoring, experiment tracking with MLflow, monitoring data drift and performance, model cards, and the regulations and responsible-AI practices that apply in India and globally.PDF
28. 28. ML Capstone: End-to-End Churn Prediction and Retention StrategyAdvanced 180 min Put the whole ML track together. Frame the churn problem, build leak-free features, compare baselines and models with cross-validation, tune the winner, choose a profit-maximising threshold under a budget, explain predictions, design segment-level retention actions with an A/B test, and write the model card, deployment plan and executive summary for your portfolio.PDF

How LLMs work, prompting, LLM APIs in Python, embeddings and vector search, RAG, tool use, agents, MCP, evaluation, guardrails, fine-tuning and responsible AI.

1. 1. How Large Language Models Work: Tokens, Embeddings, Transformers and TrainingBeginner 100 min Open the black box. Tokens and tokenisation (built from scratch), next-token prediction and sampling with temperature, embeddings, attention and the transformer, pre-training, instruction tuning and RLHF, context windows, reasoning models, why LLMs hallucinate, and how to choose among Claude, GPT, Gemini, Llama, Mistral, DeepSeek and Qwen.PDF
2. 2. Prompt Engineering in Depth: Patterns, Templates, Few-Shot, Reasoning and Structured OutputBeginner 110 min Write prompts that work reliably in products, not just in chat. The anatomy of a production prompt (role, context, task, constraints, format, examples), XML tags and delimiters, few-shot examples chosen by similarity, step-by-step reasoning and extended thinking, structured JSON output with validation, prompt templates and versioning, prompt chaining, and a simple evaluation loop.PDF
3. 3. Calling LLM APIs from Python: Messages, Streaming, Structured Output, Costs and ReliabilityIntermediate 120 min Move from the chat window to code. Set up API keys safely, send messages with system prompts, hold multi-turn conversations, stream responses, get schema-valid JSON, control reasoning effort, cache long prompts, count tokens and estimate cost, batch large jobs, handle errors and retries, and run the same pattern with other providers, with a runnable offline simulator for practice.PDF
4. 4. Embeddings and Vector Search: Meaning as Numbers, Similarity and Vector DatabasesIntermediate 110 min Learn the foundation of semantic search and RAG. What embeddings are, how word and sentence embeddings are learned (trained here from scratch on our reviews), cosine similarity, document embeddings, building a vector index, exact vs approximate nearest-neighbour search (IVF, HNSW), evaluating retrieval with recall@k, hybrid keyword + vector search, and choosing embedding models and vector databases.PDF
5. 5. Retrieval-Augmented Generation (RAG): Build, Evaluate and Improve a Q&A AssistantIntermediate 140 min Ground an LLM in your own documents. RAG architecture, document loading and chunking, indexing, hybrid retrieval and re-ranking, prompt assembly with citations, answer generation (offline simulator plus real Claude code with the citations feature), evaluation of retrieval and answers, failure modes and advanced patterns (query rewriting, contextual retrieval, metadata filters, agentic and graph RAG), built end to end on a retailer's policy handbook.PDF
6. 6. Tool Use and Function Calling: Letting LLMs Query Data, Call APIs and Take ActionsIntermediate 120 min Connect a model to the outside world. How function calling works (tool definitions, JSON schemas, tool_use and tool_result blocks), the request–execute–respond loop, writing good tool descriptions, strict schemas and validation, parallel calls, error handling, server-side tools (web search, code execution), and safety controls, with a runnable sales-analytics assistant that answers questions by calling SQL and calculator tools.PDF
7. 7. AI Agents and Agentic AI: Loops, Planning, Memory, Multi-Agent Systems and When to Use ThemIntermediate 120 min Understand what makes software "agentic". Workflows vs agents, the agent loop (observe, think, act), ReAct-style reasoning with tools, planning and task decomposition, short- and long-term memory, guardrails and stopping conditions, multi-agent patterns (orchestrator–workers, evaluator–optimiser, routing), the 2026 agent platforms, and how to decide whether a problem needs an agent at all, with a runnable data-analysis agent.PDF
8. 8. Model Context Protocol (MCP): Connecting AI Assistants to Your Data and ToolsIntermediate 100 min Learn the open standard that lets any AI application use any tool or data source. MCP architecture (hosts, clients, servers), the three primitives (tools, resources, prompts), transports (stdio and Streamable HTTP), building and testing a real MCP server for sales data in Python, connecting it to Claude Desktop, Claude Code and other clients, using remote MCP servers from the API, and securing MCP deployments.PDF
9. 9. Evaluating LLM Applications and Building Guardrails: Test Sets, LLM-as-Judge, Injection, PII and SafetyIntermediate 120 min Ship GenAI features you can trust. Build evaluation sets, choose metrics (exact match, rubric scores, groundedness), calibrate LLM-as-judge against humans, run regression tests on every prompt change, and protect applications with input and output guardrails: prompt-injection defences, PII detection and redaction (Indian identifiers), schema validation, refusal handling, red-teaming and production monitoring.PDF
10. 10. Customising Models: Prompting vs RAG vs Fine-Tuning, LoRA, Distillation and Open-Weight ModelsAdvanced 100 min Decide how to adapt a model to your task. The customisation ladder (prompting, few-shot, RAG, tools, fine-tuning, training), what fine-tuning is good and bad at, preparing training data, parameter-efficient fine-tuning with LoRA and QLoRA (with the maths), distillation from a large to a small model, running open-weight models locally with Ollama and at scale with vLLM, and the cost, privacy and quality trade-offs of API vs self-hosting.PDF
11. 11. GenAI for Data Work: Text-to-SQL, AI Coding Assistants, Data Cleaning and Automated InsightsIntermediate 120 min Apply LLMs to everyday analytics and engineering. Schema-aware text-to-SQL with guardrails and execution-accuracy evaluation, AI coding assistants and agents in notebooks and IDEs, LLM-assisted data cleaning (normalising messy categories, extracting fields from text), generating narrative insights from tables safely, and a workflow that keeps the analyst in control.PDF
12. 12. Building and Shipping a GenAI App: Architecture, Streamlit/FastAPI, Cost, Observability and Responsible AI (Capstone)Intermediate 150 min Put the GenAI track together. Reference architecture for an LLM application, a support-assistant capstone (RAG + tools + guardrails + evaluation), a Streamlit chat front end and a FastAPI back end, session memory, streaming, cost and latency budgets, caching, observability and tracing, deployment options, a launch checklist and responsible-AI governance, with a runnable cost and capacity planner.PDF

No-code track for PMs, BAs and senior PMs: APIs, MCP with Claude, how AI agents work and how to build them, connecting apps with n8n, Make, Zapier and Replit, a complete Claude Code tutorial, and voice agents and voice cloning.

1. 1. APIs Without Code: How Apps Talk, REST, JSON, Authentication, Webhooks and Postman for PMs and BAsBeginner 120 min Understand APIs well enough to write requirements, read documentation, test endpoints and design integrations without writing code. Learn requests and responses, REST verbs and status codes, JSON, authentication (API keys, OAuth 2.0), rate limits and pagination, webhooks vs polling, and how to try any API in Postman, then turn it into a PRD-ready integration spec.PDF
2. 2. MCP (Model Context Protocol) Without Code: Connect Claude to Your Apps and DataBeginner to Intermediate 120 min Learn what the Model Context Protocol is and why it became the standard way to connect AI assistants to tools and data. Understand hosts, clients, servers, tools, resources and prompts; use ready-made connectors in Claude (Google Drive, Gmail, Slack, GitHub, Notion, Jira); add local and remote MCP servers; expose your own workflows as MCP tools with n8n or Zapier without code; and manage security and permissions.PDF
3. 3. How AI Agents Work and How to Build One Without CodeIntermediate 150 min Understand the agent loop (perceive, reason, act, observe), the building blocks of an agent (model, instructions, tools, memory, knowledge, guardrails), the main design patterns, and when not to use an agent. Then build agents without code in Claude (Projects, Skills, connectors), n8n, Make, Zapier Agents and Replit Agent, and evaluate them like a product.PDF
4. 4. Connect All Your Apps with Claude, n8n, Make, Zapier and ReplitIntermediate 150 min Design integrations across your tools with Claude as the brain and n8n, Make, Zapier or Replit as the hands. Learn the four integration patterns, how to pick a platform, how Claude helps you design and debug automations, and build three end-to-end projects - a lead pipeline, a feedback-to-roadmap loop and a weekly executive report - with costs, reliability and security in mind.PDF
5. 5. Claude Code Complete Tutorial: From First Prompt to Agents, MCP, Hooks and AutomationBeginner to Advanced 180 min Learn Claude Code, Anthropic's agentic coding tool, from installation to advanced workflows. Work in the terminal, IDE, desktop and web; give context with CLAUDE.md; use plan mode, permissions and checkpoints; run slash commands, custom commands and Skills; add MCP servers; automate with hooks, subagents, headless mode and GitHub Actions; and follow best practices that work for PMs, analysts and engineers.PDF
6. 6. Voice Agents and Voice Cloning: Tools, Architecture, Build Steps and Responsible UseIntermediate 120 min Learn how AI voice agents work (speech-to-text, LLM, text-to-speech, turn-taking, telephony), compare the main platforms (ElevenLabs, Vapi, Retell AI, Bland AI, Synthflow, OpenAI Realtime, Twilio), build a no-code appointment-booking voice agent connected to your calendar and CRM, clone a voice with consent, and design for latency, Indian languages, compliance and quality.PDF

End-to-end capstone, portfolio and GitHub, analyst and data scientist interview preparation with model answers, and a 90-day plan.

1. 1. Portfolio, GitHub and Resume for Data RolesBeginner 90 min Turn your learning into evidence employers trust. Choose three to five portfolio projects, structure them on GitHub with strong READMEs, publish dashboards and apps, write an ATS-friendly one-page resume with quantified bullets, and optimise LinkedIn and Naukri profiles for analyst, data scientist, data engineer and GenAI roles.PDF
2. 2. Data Analyst Interview Preparation: SQL, Excel, Power BI, Statistics and Case QuestionsIntermediate 120 min Prepare for analyst interviews with the question types that appear most often, model answers and practice links: SQL coding rounds (joins, aggregation, window functions), Excel and Power BI questions, statistics and A/B testing, metric and product-sense cases, and behavioural questions with the STAR method.PDF
3. 3. Data Scientist, ML and GenAI Interview Preparation: Questions and Model AnswersAdvanced 120 min A compact question bank with model answers for data scientist, ML engineer and GenAI roles: Python and pandas, statistics, ML fundamentals, model evaluation, feature engineering, system and case design (churn, fraud, recommendations), MLOps, LLMs, RAG and agents, plus how to handle take-home assignments and live coding.PDF
4. 4. Your 90-Day Plan: Learning Roadmap, Job Search Strategy and Growing in Your First RoleBeginner 60 min Turn this handbook into a job. A week-by-week 90-day study and project plan for analyst, data science and GenAI tracks, a weekly routine, a job-search funnel with referrals and outreach messages, salary research and negotiation basics, and habits that help you grow in the first 6 months on the job.PDF

Prompt engineering is how you ask; context engineering is what the AI gets to see when it answers: your instructions, examples, documents, data, memory and tools. Getting both right is the first and most useful AI skill.

1. Basic Class 1: From prompts to contextSee why the information you give the AI matters more than clever wording.
2. Basic Class 2: Prompt patterns that workUse proven patterns: examples, step-by-step, roles, critique and iteration.
3. Intermediate Class 3: Structured outputs: JSON, tables and fixed sectionsGet predictable output other tools and people can use directly.
4. Intermediate Class 4: Task decomposition and chainingBreak big jobs into steps the AI can do reliably.
5. Intermediate Class 5: Long context: compression, summaries and memoryKeep long projects accurate when chats and documents get large.
6. Intermediate Class 6: Documents, PDFs, scans and OCRGet reliable information out of PDFs, slides, spreadsheets and images.
7. Advanced Class 7: Source attribution, fact-checking and knowledge graphsMake AI outputs traceable to evidence and organise knowledge by relationships.
8. Advanced Class 8: Context engineering for products and agentsDesign what your AI feature or agent sees, and test it.

An eval is a repeatable test that measures how well an AI feature does its job on a fixed set of examples, with a clear way to score each answer. It's the quality bar, the launch gate and the product spec for AI.

1. Basic Class 1: What an eval is, and why PMs own itUnderstand evals as the unit test, the quality bar and the product spec for AI features.
2. Basic Class 2: Defining success: criteria, rubrics and failure modesTurn a fuzzy goal ('helpful answers') into criteria anyone can score the same way.
3. Basic Class 3: Building the eval dataset (golden set)Create a test set that represents real users, including the hard and risky cases.
4. Intermediate Class 4: Grading methods: code, humans and LLM-as-judgeChoose the cheapest grader that measures what matters, and know when to use humans.
5. Intermediate Class 5: Error analysis: finding what actually breaksRead outputs systematically, name the failure modes and fix the biggest ones first.
6. Intermediate Class 6: Evaluating RAG (retrieval-augmented generation)Measure retrieval and generation separately, so you know which half to fix.
7. Advanced Class 7: Evaluating agents and tool useMeasure multi-step AI agents on outcomes, the path they took, cost and safety.
8. Advanced Class 8: Safety, responsible AI and red-teamingTest for harm, privacy leaks, bias and jailbreaks before attackers and journalists do.
9. Advanced Class 9: Online evals: A/B tests, monitoring and regression gatesConnect offline scores to real user value and catch quality drops in production.
10. Advanced Class 10: Eval ops: tools, cost, the AI PRD and your capstoneRun evals as a product practice: tooling, budgets, documentation and team rituals.

Orchestration is how AI steps, tools and people are connected into a workflow that runs reliably; monitoring is how you know it keeps working. Start with a simple workflow and add agents only where they are needed.

1. Basic Class 1: Workflows vs agents: start simpleKnow when a fixed workflow is enough and when an agent is worth it.
2. Basic Class 2: Core patterns: chaining, routing and parallelisationUse the three most common orchestration patterns.
3. Intermediate Class 3: Orchestrator-workers and evaluator-optimizerUse the two most powerful agentic patterns safely.
4. Intermediate Class 4: State, checkpoints and long-running workMake workflows resumable and traceable.
5. Intermediate Class 5: Human-in-the-loop, approvals and error recoveryPut people at the right points and recover from failures safely.
6. Advanced Class 6: Multi-agent systemsKnow when splitting work across agents helps, and how to coordinate them.
7. Advanced Class 7: Triggers, scheduling and cost controlRun workflows automatically and within budget.
8. Advanced Class 8: Monitoring, observability and the final projectKnow when a workflow breaks or degrades, and fix it fast.

Webhooks tell your system the moment something happens; triggers decide when automations run; browser automation does the clicking when there's no API; and the Playwright MCP lets an AI assistant like Claude use a real browser for you. Together they're the most-used web automation stack in 2026.

1. Basic Class 1: The web-automation stack: APIs, webhooks, triggers, browsersSee how the pieces fit, and pick the right one for each job.
2. Basic Class 2: Webhooks explained (with real examples)Understand webhooks and receive your first one.
3. Intermediate Class 3: Building reliable webhook receiversVerify, process and retry webhooks safely in production.
4. Basic Class 4: Triggers: schedules, events, polling and manualChoose how and when automations start.
5. Intermediate Class 5: Playwright for browser automationAutomate any website reliably with Playwright.
6. Intermediate Class 6: Playwright MCP: let Claude and other AI assistants use a browserInstall and use the Playwright MCP server with Claude Code, Claude Desktop, Cursor and VS Code.
7. Intermediate Class 7: Other browser-automation frameworks and MCPsKnow the alternatives and when each wins.
8. Advanced Class 8: AI browser agents and cloud browsersUse AI-native automation tools and hosted browsers at scale.
9. Advanced Class 9: Workflow platforms: n8n, Zapier, Make, Pipedream, RPA and scrapersRun webhooks, triggers and browser steps as complete workflows.
10. Advanced Class 10: Production: security, legality, monitoring, evals and capstoneRun web automations safely and prove they work.

Browser automation means getting software, and now AI, to use websites for you: open pages, click, type, fill forms, collect data and complete tasks. Classic scripts follow fixed steps; AI browser agents take a goal in plain English and decide each step themselves.

1. Basic Class 1: What browser automation is (and when to use it)Understand the three generations of web automation and pick the right one for a task.
2. Basic Class 2: No-code browser automationAutomate simple web tasks without writing code.
3. Intermediate Class 3: Playwright for PMs and analystsRead and write a simple browser script, so you can work with engineers and automate small tasks.
4. Intermediate Class 4: AI browser agents and computer useUnderstand how AI agents use browsers and computers, and how to give them good instructions.
5. Intermediate Class 5: Deep research agents and web research workflowsUse AI research agents for market and competitor research, and verify what they find.
6. Advanced Class 6: Real workflows: PM, analyst, marketing and opsDesign end-to-end automations that combine agents, scripts and no-code tools.
7. Advanced Class 7: Safety, security, ethics and the lawPrevent the main risks of browser agents: injection, wrong actions, data leaks and rule-breaking.
8. Advanced Class 8: Evaluating and productising browser agentsMeasure agent quality and write a PRD for a browser-agent feature; complete the final project.

AI builder tools let PMs and non-coders research, write, prototype, build websites and apps, and connect tools together. This course shows what each tool is best at and walks you through real builds step by step.

1. Basic Class 1: Choosing the right AI toolKnow the four kinds of AI tools and when to use each.
2. Basic Class 2: Claude for PMs: what you can do and howUse Claude's Projects, Artifacts, research, connectors and Skills in daily PM work.
3. Basic Class 3: ChatGPT for PMs: Projects, GPTs, Canvas, research and agentUse ChatGPT's main features for research, documents, data and automation.
4. Basic Class 4: Gemini for PMs: Gemini app, Gems, Deep Research, NotebookLM, Workspace, AI StudioUse Google's AI tools, especially if your team lives in Google Workspace.
5. Intermediate Class 5: Lovable: from idea to a working app (no code)Build and publish an app with Lovable, including a database, login and your own domain.
6. Intermediate Class 6: Claude Code and OpenAI Codex: coding agents for PMsUse coding agents to build and change real websites and apps, safely.
7. Intermediate Class 7: Tutorial: create a website, add pages and publish on your domainFollow one complete path from idea to a live site you own.
8. Intermediate Class 8: Connect your tools: Zapier, Make, n8n, webhooks and MCPMake your website and tools talk to each other.
9. Advanced Class 9: Connect other websites and servicesEmbed, integrate and call other websites safely.
10. Advanced Class 10: PM workflows end to end and capstoneCombine tools into a real product workflow.

Before LLMs, most AI in products was machine learning that predicts, classifies, ranks or recommends (fraud scores, feeds, ETAs). PMs still own these products: the problem, the data, the metric trade-offs and the lifecycle.

1. Basic Class 1: ML problem typesRecognise which ML problem you're solving.
2. Basic Class 2: Data and labellingGet the data an ML product needs.
3. Intermediate Class 3: Metrics and trade-offsChoose model metrics that match business costs.
4. Intermediate Class 4: Recommendations and personalisationUnderstand how recommenders work and are measured.
5. Advanced Class 5: Model lifecycle and MLOpsKeep models working after launch.
6. Advanced Class 6: Working with data scientists and final projectCollaborate well and ship an ML feature.

AI governance is the set of policies, reviews and controls that make sure AI is safe, fair, legal and accountable, so teams can ship faster with fewer surprises.

1. Basic Class 1: Principles and risksKnow the main responsible-AI principles and risks.
2. Basic Class 2: Regulations to knowUnderstand the main laws and frameworks at a PM level.
3. Intermediate Class 3: AI risk and impact assessmentAssess a feature before launch.
4. Intermediate Class 4: Policies and documentationWrite the documents governance needs.
5. Advanced Class 5: Operating governanceRun governance without slowing teams down.
6. Advanced Class 6: Final projectGovern a real AI feature.

A platform PM builds products that other teams, developers or partners build on: APIs, SDKs, internal platforms and ecosystems. The users are developers, and the product is how easily they succeed.

1. Basic Class 1: Platforms and APIs as productsUnderstand platform types and users.
2. Basic Class 2: Developer experience (DX)Make developers successful fast.
3. Intermediate Class 3: API design decisions for PMsDecide what matters in API contracts.
4. Intermediate Class 4: Ecosystems and partnersGrow a platform through others.
5. Advanced Class 5: Platform metricsMeasure platform health.
6. Advanced Class 6: AI platforms and final projectApply platform thinking to AI.

AI product strategy is deciding which problems AI should solve for your users and business, how you'll win (data, distribution, workflow, trust), and what to build, buy or partner for, before writing a single PRD.

1. Basic Class 1: Where AI creates valueSeparate real AI opportunities from hype.
2. Basic Class 2: Moats and data flywheelsUnderstand what makes an AI product hard to copy.
3. Intermediate Class 3: Build vs buy vs partner, and choosing modelsMake sourcing and model decisions with clear trade-offs.
4. Intermediate Class 4: Assessing and prioritising AI opportunitiesScore AI ideas on value, feasibility and risk.
5. Advanced Class 5: AI roadmaps and portfolio of betsPlan AI work under uncertainty.
6. Advanced Class 6: Case studies and final projectApply the strategy tools end to end.

Generative AI for analysts: prompting, AI vs GenAI vs agentic AI, AI over SQL (Metabase, MinusX), Copilot and Gemini in Excel, and office agents.

1. 1. Generative AI for Analysts: How It Works, Prompting & AI vs GenAI vs Agentic AIBeginner 110 min Understand what generative AI is and how large language models behave (tokens, context, temperature, hallucination), how AI, machine learning, deep learning, GenAI and agentic AI differ, and how to write prompts that give analysts accurate, checkable results, with reusable templates and privacy rules.PDF
2. 2. AI with SQL: Text-to-SQL, Docker, Metabase & MinusXIntermediate 130 min Use AI to write, explain and debug SQL safely. Learn how text-to-SQL works and where it fails, give the model the right schema, protect your database with read-only access, install Docker, run Metabase on your own data, connect an AI assistant such as MinusX, and validate every generated query.PDF
3. 3. AI in Excel & Office: Copilot, Gemini, Claude, Python in Excel & AgentsIntermediate 120 min Use AI where analysts work every day. Work with Copilot in Excel (formulas, insights, charts, the COPILOT function), Gemini in Google Sheets and Excel add-ins, Claude and other assistants for formulas and files, Python in Excel, and office agents that perform multi-step tasks, and learn how to prompt, verify and stay safe.PDF
4. 4. The AI-Assisted Analyst Workflow, Agents & Governance (Capstone)Intermediate 140 min Combine everything into one repeatable workflow: frame a question, brief the AI, analyse with code, verify, visualise and write the story. See how an analyst agent works (plan, tools, observe, repeat) with a runnable toy example, learn MCP and tool-calling basics, and finish with governance, ethics and a telecom-churn capstone.PDF

How LLMs work, prompting, LLM APIs in Python, embeddings and vector search, RAG, tool use, agents, MCP, evaluation, guardrails, fine-tuning and responsible AI.

1. 1. How Large Language Models Work: Tokens, Embeddings, Transformers and TrainingBeginner 100 min Open the black box. Tokens and tokenisation (built from scratch), next-token prediction and sampling with temperature, embeddings, attention and the transformer, pre-training, instruction tuning and RLHF, context windows, reasoning models, why LLMs hallucinate, and how to choose among Claude, GPT, Gemini, Llama, Mistral, DeepSeek and Qwen.PDF
2. 2. Prompt Engineering in Depth: Patterns, Templates, Few-Shot, Reasoning and Structured OutputBeginner 110 min Write prompts that work reliably in products, not just in chat. The anatomy of a production prompt (role, context, task, constraints, format, examples), XML tags and delimiters, few-shot examples chosen by similarity, step-by-step reasoning and extended thinking, structured JSON output with validation, prompt templates and versioning, prompt chaining, and a simple evaluation loop.PDF
3. 3. Calling LLM APIs from Python: Messages, Streaming, Structured Output, Costs and ReliabilityIntermediate 120 min Move from the chat window to code. Set up API keys safely, send messages with system prompts, hold multi-turn conversations, stream responses, get schema-valid JSON, control reasoning effort, cache long prompts, count tokens and estimate cost, batch large jobs, handle errors and retries, and run the same pattern with other providers, with a runnable offline simulator for practice.PDF
4. 4. Embeddings and Vector Search: Meaning as Numbers, Similarity and Vector DatabasesIntermediate 110 min Learn the foundation of semantic search and RAG. What embeddings are, how word and sentence embeddings are learned (trained here from scratch on our reviews), cosine similarity, document embeddings, building a vector index, exact vs approximate nearest-neighbour search (IVF, HNSW), evaluating retrieval with recall@k, hybrid keyword + vector search, and choosing embedding models and vector databases.PDF
5. 5. Retrieval-Augmented Generation (RAG): Build, Evaluate and Improve a Q&A AssistantIntermediate 140 min Ground an LLM in your own documents. RAG architecture, document loading and chunking, indexing, hybrid retrieval and re-ranking, prompt assembly with citations, answer generation (offline simulator plus real Claude code with the citations feature), evaluation of retrieval and answers, failure modes and advanced patterns (query rewriting, contextual retrieval, metadata filters, agentic and graph RAG), built end to end on a retailer's policy handbook.PDF
6. 6. Tool Use and Function Calling: Letting LLMs Query Data, Call APIs and Take ActionsIntermediate 120 min Connect a model to the outside world. How function calling works (tool definitions, JSON schemas, tool_use and tool_result blocks), the request–execute–respond loop, writing good tool descriptions, strict schemas and validation, parallel calls, error handling, server-side tools (web search, code execution), and safety controls, with a runnable sales-analytics assistant that answers questions by calling SQL and calculator tools.PDF
7. 7. AI Agents and Agentic AI: Loops, Planning, Memory, Multi-Agent Systems and When to Use ThemIntermediate 120 min Understand what makes software "agentic". Workflows vs agents, the agent loop (observe, think, act), ReAct-style reasoning with tools, planning and task decomposition, short- and long-term memory, guardrails and stopping conditions, multi-agent patterns (orchestrator–workers, evaluator–optimiser, routing), the 2026 agent platforms, and how to decide whether a problem needs an agent at all, with a runnable data-analysis agent.PDF
8. 8. Model Context Protocol (MCP): Connecting AI Assistants to Your Data and ToolsIntermediate 100 min Learn the open standard that lets any AI application use any tool or data source. MCP architecture (hosts, clients, servers), the three primitives (tools, resources, prompts), transports (stdio and Streamable HTTP), building and testing a real MCP server for sales data in Python, connecting it to Claude Desktop, Claude Code and other clients, using remote MCP servers from the API, and securing MCP deployments.PDF
9. 9. Evaluating LLM Applications and Building Guardrails: Test Sets, LLM-as-Judge, Injection, PII and SafetyIntermediate 120 min Ship GenAI features you can trust. Build evaluation sets, choose metrics (exact match, rubric scores, groundedness), calibrate LLM-as-judge against humans, run regression tests on every prompt change, and protect applications with input and output guardrails: prompt-injection defences, PII detection and redaction (Indian identifiers), schema validation, refusal handling, red-teaming and production monitoring.PDF
10. 10. Customising Models: Prompting vs RAG vs Fine-Tuning, LoRA, Distillation and Open-Weight ModelsAdvanced 100 min Decide how to adapt a model to your task. The customisation ladder (prompting, few-shot, RAG, tools, fine-tuning, training), what fine-tuning is good and bad at, preparing training data, parameter-efficient fine-tuning with LoRA and QLoRA (with the maths), distillation from a large to a small model, running open-weight models locally with Ollama and at scale with vLLM, and the cost, privacy and quality trade-offs of API vs self-hosting.PDF
11. 11. GenAI for Data Work: Text-to-SQL, AI Coding Assistants, Data Cleaning and Automated InsightsIntermediate 120 min Apply LLMs to everyday analytics and engineering. Schema-aware text-to-SQL with guardrails and execution-accuracy evaluation, AI coding assistants and agents in notebooks and IDEs, LLM-assisted data cleaning (normalising messy categories, extracting fields from text), generating narrative insights from tables safely, and a workflow that keeps the analyst in control.PDF
12. 12. Building and Shipping a GenAI App: Architecture, Streamlit/FastAPI, Cost, Observability and Responsible AI (Capstone)Intermediate 150 min Put the GenAI track together. Reference architecture for an LLM application, a support-assistant capstone (RAG + tools + guardrails + evaluation), a Streamlit chat front end and a FastAPI back end, session memory, streaming, cost and latency budgets, caching, observability and tracing, deployment options, a launch checklist and responsible-AI governance, with a runnable cost and capacity planner.PDF

No-code track for PMs, BAs and senior PMs: APIs, MCP with Claude, how AI agents work and how to build them, connecting apps with n8n, Make, Zapier and Replit, a complete Claude Code tutorial, and voice agents and voice cloning.

1. 1. APIs Without Code: How Apps Talk, REST, JSON, Authentication, Webhooks and Postman for PMs and BAsBeginner 120 min Understand APIs well enough to write requirements, read documentation, test endpoints and design integrations without writing code. Learn requests and responses, REST verbs and status codes, JSON, authentication (API keys, OAuth 2.0), rate limits and pagination, webhooks vs polling, and how to try any API in Postman, then turn it into a PRD-ready integration spec.PDF
2. 2. MCP (Model Context Protocol) Without Code: Connect Claude to Your Apps and DataBeginner to Intermediate 120 min Learn what the Model Context Protocol is and why it became the standard way to connect AI assistants to tools and data. Understand hosts, clients, servers, tools, resources and prompts; use ready-made connectors in Claude (Google Drive, Gmail, Slack, GitHub, Notion, Jira); add local and remote MCP servers; expose your own workflows as MCP tools with n8n or Zapier without code; and manage security and permissions.PDF
3. 3. How AI Agents Work and How to Build One Without CodeIntermediate 150 min Understand the agent loop (perceive, reason, act, observe), the building blocks of an agent (model, instructions, tools, memory, knowledge, guardrails), the main design patterns, and when not to use an agent. Then build agents without code in Claude (Projects, Skills, connectors), n8n, Make, Zapier Agents and Replit Agent, and evaluate them like a product.PDF
4. 4. Connect All Your Apps with Claude, n8n, Make, Zapier and ReplitIntermediate 150 min Design integrations across your tools with Claude as the brain and n8n, Make, Zapier or Replit as the hands. Learn the four integration patterns, how to pick a platform, how Claude helps you design and debug automations, and build three end-to-end projects - a lead pipeline, a feedback-to-roadmap loop and a weekly executive report - with costs, reliability and security in mind.PDF
5. 5. Claude Code Complete Tutorial: From First Prompt to Agents, MCP, Hooks and AutomationBeginner to Advanced 180 min Learn Claude Code, Anthropic's agentic coding tool, from installation to advanced workflows. Work in the terminal, IDE, desktop and web; give context with CLAUDE.md; use plan mode, permissions and checkpoints; run slash commands, custom commands and Skills; add MCP servers; automate with hooks, subagents, headless mode and GitHub Actions; and follow best practices that work for PMs, analysts and engineers.PDF
6. 6. Voice Agents and Voice Cloning: Tools, Architecture, Build Steps and Responsible UseIntermediate 120 min Learn how AI voice agents work (speech-to-text, LLM, text-to-speech, turn-taking, telephony), compare the main platforms (ElevenLabs, Vapi, Retell AI, Bland AI, Synthflow, OpenAI Realtime, Twilio), build a no-code appointment-booking voice agent connected to your calendar and CRM, clone a voice with consent, and design for latency, Indian languages, compliance and quality.PDF

Complete tutorials for the design and automation tools used across the programs: Figma (design systems, prototypes, Dev Mode, Figma Make) and n8n (workflows, APIs, AI agents, MCP).

1. 1. Figma Complete Tutorial: Interface, Auto Layout, Components, Variables, Prototypes, Dev Mode and Figma AIBeginner to Advanced 180 min Learn Figma from zero to a handoff-ready design. Tour the interface, master frames, shapes, text and the 8-point grid, build responsive layouts with auto layout and constraints, create components with variants and properties, use styles and variables (light and dark modes), prototype flows with Smart Animate and overlays, hand off in Dev Mode, run workshops in FigJam, and generate working prototypes with Figma AI and Figma Make. Includes a dashboard-design project for data roles.PDF
2. 2. n8n Complete Tutorial: Workflow Automation, Data Pipelines and AI AgentsBeginner to Advanced 180 min Learn n8n, the open, self-hostable workflow-automation tool. Set it up on n8n Cloud or Docker, understand triggers, nodes, credentials, items and expressions, transform data with Edit Fields, IF, Switch, Merge, Loop and Code nodes, call any API with HTTP Request, handle errors and schedules, build AI agents with chat models, memory, tools and vector stores, and connect n8n to MCP. Four complete projects: lead capture, a daily sales report, an email-triage AI agent and a document Q&A bot.PDF

AI builder tools let PMs and non-coders research, write, prototype, build websites and apps, and connect tools together. This course shows what each tool is best at and walks you through real builds step by step.

1. Basic Class 1: Choosing the right AI toolKnow the four kinds of AI tools and when to use each.
2. Basic Class 2: Claude for PMs: what you can do and howUse Claude's Projects, Artifacts, research, connectors and Skills in daily PM work.
3. Basic Class 3: ChatGPT for PMs: Projects, GPTs, Canvas, research and agentUse ChatGPT's main features for research, documents, data and automation.
4. Basic Class 4: Gemini for PMs: Gemini app, Gems, Deep Research, NotebookLM, Workspace, AI StudioUse Google's AI tools, especially if your team lives in Google Workspace.
5. Intermediate Class 5: Lovable: from idea to a working app (no code)Build and publish an app with Lovable, including a database, login and your own domain.
6. Intermediate Class 6: Claude Code and OpenAI Codex: coding agents for PMsUse coding agents to build and change real websites and apps, safely.
7. Intermediate Class 7: Tutorial: create a website, add pages and publish on your domainFollow one complete path from idea to a live site you own.
8. Intermediate Class 8: Connect your tools: Zapier, Make, n8n, webhooks and MCPMake your website and tools talk to each other.
9. Advanced Class 9: Connect other websites and servicesEmbed, integrate and call other websites safely.
10. Advanced Class 10: PM workflows end to end and capstoneCombine tools into a real product workflow.

Webhooks tell your system the moment something happens; triggers decide when automations run; browser automation does the clicking when there's no API; and the Playwright MCP lets an AI assistant like Claude use a real browser for you. Together they're the most-used web automation stack in 2026.

1. Basic Class 1: The web-automation stack: APIs, webhooks, triggers, browsersSee how the pieces fit, and pick the right one for each job.
2. Basic Class 2: Webhooks explained (with real examples)Understand webhooks and receive your first one.
3. Intermediate Class 3: Building reliable webhook receiversVerify, process and retry webhooks safely in production.
4. Basic Class 4: Triggers: schedules, events, polling and manualChoose how and when automations start.
5. Intermediate Class 5: Playwright for browser automationAutomate any website reliably with Playwright.
6. Intermediate Class 6: Playwright MCP: let Claude and other AI assistants use a browserInstall and use the Playwright MCP server with Claude Code, Claude Desktop, Cursor and VS Code.
7. Intermediate Class 7: Other browser-automation frameworks and MCPsKnow the alternatives and when each wins.
8. Advanced Class 8: AI browser agents and cloud browsersUse AI-native automation tools and hosted browsers at scale.
9. Advanced Class 9: Workflow platforms: n8n, Zapier, Make, Pipedream, RPA and scrapersRun webhooks, triggers and browser steps as complete workflows.
10. Advanced Class 10: Production: security, legality, monitoring, evals and capstoneRun web automations safely and prove they work.

Prompt engineering is how you ask; context engineering is what the AI gets to see when it answers: your instructions, examples, documents, data, memory and tools. Getting both right is the first and most useful AI skill.

1. Basic Class 1: From prompts to contextSee why the information you give the AI matters more than clever wording.
2. Basic Class 2: Prompt patterns that workUse proven patterns: examples, step-by-step, roles, critique and iteration.
3. Intermediate Class 3: Structured outputs: JSON, tables and fixed sectionsGet predictable output other tools and people can use directly.
4. Intermediate Class 4: Task decomposition and chainingBreak big jobs into steps the AI can do reliably.
5. Intermediate Class 5: Long context: compression, summaries and memoryKeep long projects accurate when chats and documents get large.
6. Intermediate Class 6: Documents, PDFs, scans and OCRGet reliable information out of PDFs, slides, spreadsheets and images.
7. Advanced Class 7: Source attribution, fact-checking and knowledge graphsMake AI outputs traceable to evidence and organise knowledge by relationships.
8. Advanced Class 8: Context engineering for products and agentsDesign what your AI feature or agent sees, and test it.

A skill is a reusable method you teach an AI once (instructions, templates, examples, scripts) so it does a recurring task the same good way every time. A tool gives the AI access or actions; memory and evals are parts of the system around it.

1. Basic Class 1: Skills, tools, memory and evals: the systemKnow the parts of an AI assistant and where a skill fits.
2. Basic Class 2: Templates: the foundation of every skillDesign templates that make outputs consistent and complete.
3. Intermediate Class 3: Where to build skills: Projects, GPTs, Gems and Agent SkillsChoose the right place to package a skill.
4. Intermediate Class 4: Writing great skill instructionsWrite instructions that produce consistent, high-quality results.
5. Intermediate Class 5: A PM skill libraryBuild a set of skills that covers your core PM work.
6. Advanced Class 6: Connecting skills to sources and toolsGround skills in your documents and let them act through tools.
7. Advanced Class 7: Testing skills with evalsProve a skill works and stays working.
8. Advanced Class 8: Project: case study → PRD → presentation skillBuild a portfolio-ready reusable skill end to end (e.g. for a VitaFit-style assignment).

A second brain is a personal knowledge system outside your head: notes, documents, meetings and ideas stored in one trusted place. With AI, it becomes something you can talk to: ask questions across everything you've saved and get answers with sources.

1. Basic Class 1: What a second brain is, and why AI changes itUnderstand the idea, the PARA and CODE methods, and what AI adds.
2. Basic Class 2: Capture: getting things into the systemSet up fast, low-friction capture from phone, browser, reading and voice.
3. Basic Class 3: Organise and distilTurn a pile of captures into findable, useful notes.
4. Intermediate Class 4: Ask AI across your notesUse NotebookLM, Claude and ChatGPT Projects, Notion AI and Obsidian plugins to question your knowledge.
5. Intermediate Class 5: Context engineering and AI memoryGive AI the right context about you and your work, and manage what it remembers.
6. Intermediate Class 6: Pipelines: meetings, email, reading and learningAutomatically feed your second brain from meetings, email and courses.
7. Advanced Class 7: Second brains for PMs, students and teamsApply the system to product work, exam prep and team knowledge.
8. Advanced Class 8: Build your own: MCP, automation and evaluationConnect AI assistants to your notes with MCP and automation, and make sure answers stay accurate.

An eval is a repeatable test that measures how well an AI feature does its job on a fixed set of examples, with a clear way to score each answer. It's the quality bar, the launch gate and the product spec for AI.

1. Basic Class 1: What an eval is, and why PMs own itUnderstand evals as the unit test, the quality bar and the product spec for AI features.
2. Basic Class 2: Defining success: criteria, rubrics and failure modesTurn a fuzzy goal ('helpful answers') into criteria anyone can score the same way.
3. Basic Class 3: Building the eval dataset (golden set)Create a test set that represents real users, including the hard and risky cases.
4. Intermediate Class 4: Grading methods: code, humans and LLM-as-judgeChoose the cheapest grader that measures what matters, and know when to use humans.
5. Intermediate Class 5: Error analysis: finding what actually breaksRead outputs systematically, name the failure modes and fix the biggest ones first.
6. Intermediate Class 6: Evaluating RAG (retrieval-augmented generation)Measure retrieval and generation separately, so you know which half to fix.
7. Advanced Class 7: Evaluating agents and tool useMeasure multi-step AI agents on outcomes, the path they took, cost and safety.
8. Advanced Class 8: Safety, responsible AI and red-teamingTest for harm, privacy leaks, bias and jailbreaks before attackers and journalists do.
9. Advanced Class 9: Online evals: A/B tests, monitoring and regression gatesConnect offline scores to real user value and catch quality drops in production.
10. Advanced Class 10: Eval ops: tools, cost, the AI PRD and your capstoneRun evals as a product practice: tooling, budgets, documentation and team rituals.

Browser automation means getting software, and now AI, to use websites for you: open pages, click, type, fill forms, collect data and complete tasks. Classic scripts follow fixed steps; AI browser agents take a goal in plain English and decide each step themselves.

1. Basic Class 1: What browser automation is (and when to use it)Understand the three generations of web automation and pick the right one for a task.
2. Basic Class 2: No-code browser automationAutomate simple web tasks without writing code.
3. Intermediate Class 3: Playwright for PMs and analystsRead and write a simple browser script, so you can work with engineers and automate small tasks.
4. Intermediate Class 4: AI browser agents and computer useUnderstand how AI agents use browsers and computers, and how to give them good instructions.
5. Intermediate Class 5: Deep research agents and web research workflowsUse AI research agents for market and competitor research, and verify what they find.
6. Advanced Class 6: Real workflows: PM, analyst, marketing and opsDesign end-to-end automations that combine agents, scripts and no-code tools.
7. Advanced Class 7: Safety, security, ethics and the lawPrevent the main risks of browser agents: injection, wrong actions, data leaks and rule-breaking.
8. Advanced Class 8: Evaluating and productising browser agentsMeasure agent quality and write a PRD for a browser-agent feature; complete the final project.

Orchestration is how AI steps, tools and people are connected into a workflow that runs reliably; monitoring is how you know it keeps working. Start with a simple workflow and add agents only where they are needed.

1. Basic Class 1: Workflows vs agents: start simpleKnow when a fixed workflow is enough and when an agent is worth it.
2. Basic Class 2: Core patterns: chaining, routing and parallelisationUse the three most common orchestration patterns.
3. Intermediate Class 3: Orchestrator-workers and evaluator-optimizerUse the two most powerful agentic patterns safely.
4. Intermediate Class 4: State, checkpoints and long-running workMake workflows resumable and traceable.
5. Intermediate Class 5: Human-in-the-loop, approvals and error recoveryPut people at the right points and recover from failures safely.
6. Advanced Class 6: Multi-agent systemsKnow when splitting work across agents helps, and how to coordinate them.
7. Advanced Class 7: Triggers, scheduling and cost controlRun workflows automatically and within budget.
8. Advanced Class 8: Monitoring, observability and the final projectKnow when a workflow breaks or degrades, and fix it fast.

AI product strategy is deciding which problems AI should solve for your users and business, how you'll win (data, distribution, workflow, trust), and what to build, buy or partner for, before writing a single PRD.

1. Basic Class 1: Where AI creates valueSeparate real AI opportunities from hype.
2. Basic Class 2: Moats and data flywheelsUnderstand what makes an AI product hard to copy.
3. Intermediate Class 3: Build vs buy vs partner, and choosing modelsMake sourcing and model decisions with clear trade-offs.
4. Intermediate Class 4: Assessing and prioritising AI opportunitiesScore AI ideas on value, feasibility and risk.
5. Advanced Class 5: AI roadmaps and portfolio of betsPlan AI work under uncertainty.
6. Advanced Class 6: Case studies and final projectApply the strategy tools end to end.

AI UX is designing products where outputs are probabilistic: helping users understand what AI can do, trust it the right amount, correct it easily, and recover when it's wrong.

1. Basic Class 1: Principles of AI UXDesign for uncertainty and the right level of trust.
2. Basic Class 2: Interaction patternsPick the right pattern for the job.
3. Intermediate Class 3: Trust, explainability and citationsHelp users judge AI output quickly.
4. Intermediate Class 4: Feedback, control and personalisationTurn user actions into learning signals.
5. Advanced Class 5: Failure, safety and handoff designDesign what happens when AI can't or shouldn't answer.
6. Advanced Class 6: Testing AI UX and final projectValidate AI experiences with users and evals.

AI features have real running costs on every use, so pricing must cover cost per task while matching the value customers get; that's why AI pricing is shifting from per-seat to usage and outcome models.

1. Basic Class 1: AI cost structure and unit economicsKnow what each AI task costs.
2. Basic Class 2: Pricing modelsCompare seat, usage, credits, outcome and hybrid pricing.
3. Intermediate Class 3: Packaging AI featuresDecide free, paid and add-on AI.
4. Intermediate Class 4: Protecting marginsReduce AI cost without hurting quality.
5. Advanced Class 5: Go-to-market for AI productsLaunch and sell AI with proof.
6. Advanced Class 6: Final projectPrice and package a real AI product.

Before LLMs, most AI in products was machine learning that predicts, classifies, ranks or recommends (fraud scores, feeds, ETAs). PMs still own these products: the problem, the data, the metric trade-offs and the lifecycle.

1. Basic Class 1: ML problem typesRecognise which ML problem you're solving.
2. Basic Class 2: Data and labellingGet the data an ML product needs.
3. Intermediate Class 3: Metrics and trade-offsChoose model metrics that match business costs.
4. Intermediate Class 4: Recommendations and personalisationUnderstand how recommenders work and are measured.
5. Advanced Class 5: Model lifecycle and MLOpsKeep models working after launch.
6. Advanced Class 6: Working with data scientists and final projectCollaborate well and ship an ML feature.

AI governance is the set of policies, reviews and controls that make sure AI is safe, fair, legal and accountable, so teams can ship faster with fewer surprises.

1. Basic Class 1: Principles and risksKnow the main responsible-AI principles and risks.
2. Basic Class 2: Regulations to knowUnderstand the main laws and frameworks at a PM level.
3. Intermediate Class 3: AI risk and impact assessmentAssess a feature before launch.
4. Intermediate Class 4: Policies and documentationWrite the documents governance needs.
5. Advanced Class 5: Operating governanceRun governance without slowing teams down.
6. Advanced Class 6: Final projectGovern a real AI feature.

A platform PM builds products that other teams, developers or partners build on: APIs, SDKs, internal platforms and ecosystems. The users are developers, and the product is how easily they succeed.

1. Basic Class 1: Platforms and APIs as productsUnderstand platform types and users.
2. Basic Class 2: Developer experience (DX)Make developers successful fast.
3. Intermediate Class 3: API design decisions for PMsDecide what matters in API contracts.
4. Intermediate Class 4: Ecosystems and partnersGrow a platform through others.
5. Advanced Class 5: Platform metricsMeasure platform health.
6. Advanced Class 6: AI platforms and final projectApply platform thinking to AI.

Product operations is the function that makes product teams run smoothly at scale: shared processes, tools, data and insight pipelines, so PMs spend time on decisions instead of admin.

1. Basic Class 1: What product ops isKnow the scope and value of product ops.
2. Basic Class 2: Processes and ritualsDesign lightweight rhythms.
3. Intermediate Class 3: Tools and dataRun the product tool stack and metrics.
4. Intermediate Class 4: Customer-insight pipelinesTurn feedback into prioritised insights.
5. Advanced Class 5: Launch management and governanceMake launches predictable.
6. Advanced Class 6: AI in product ops and final projectAutomate product ops with AI.

Multimodal AI understands and generates more than text: images, audio and video. It powers features like reading invoices from photos, voice assistants, product images and video explainers, along with new risks like deepfakes.

1. Basic Class 1: Multimodal basicsKnow what modern models can see, hear and create.
2. Basic Class 2: Vision understanding use casesUse AI to read and interpret images.
3. Intermediate Class 3: Image generation in productsUse generative images responsibly.
4. Intermediate Class 4: Voice and videoBuild with speech and video AI.
5. Advanced Class 5: Rights, safety, deepfakes and labellingManage the risks of AI media.
6. Advanced Class 6: Final projectShip a multimodal feature.