Learn ten modules in AI for finance. Every module ends with something you can run on your own laptop.

YOU GET THE BOOK PLUS WHOLE COURSE COMPANION MATERIAL LIKE PPTS AND LIVE LAB
Inside the book is a web address that opens a companion course page with the PowerPoint deck for all ten modules, every lab script as a single download, and four labs that run real Python directly in your browser—with nothing to install.

It is free, requires no sign-up, and uses free public data.

WHAT YOU BUILD
Pull a company's income statement from SEC EDGAR and check the numbers using Python.
Answer investor questions from a long IPO prospectus, with the source page cited for every answer.
Build AI agents that draft an investment memo and check their numbers against the original filing.
Build and backtest a trading strategy, then understand why a strategy can fail even when it looks good on paper.
Simulate possible future outcomes for a portfolio and test how reliable the risk estimate is.
Rebuild a US bank's capital ratios from its own quarterly filing.
Calculate a fund's NAV and identify exceptions before it is released.
Build a discounted cash flow model that writes its own morning note, then catch the AI when its analysis is wrong.
WHY THIS BOOK IS DIFFERENT
While writing this book, I asked a language model to write the commentary for a valuation I had just run.

It said the company's value was most sensitive to the terminal growth rate.

The valuation engine had already measured it.

The model had the ranking backwards—and wrote it with complete confidence.

That run appears in Chapter 9, printed as it came out.

The arithmetic happens in code. The words happen in the model. The judgment stays with you.

WHO THIS BOOK IS FOR
For students, analysts, risk professionals, finance professionals, and anyone who wants to understand how AI can be applied to real financial work.

No machine-learning background is required.

You do not need to be an expert in finance to follow the book. Concepts are introduced from the ground up, explained in plain English, and then turned into practical projects.

Written by Tejas Jadhav, CFA, FRM, who teaches AI in Finance across multiple institutions and has 12+ years of experience in finance, banking, and AI implementation in BFSI.

Education only. Every trading figure is a backtest, every risk figure is a simulation using stated dates, and every worked example is labelled as constructed. Nothing in this book constitutes investment advice.