You already write software. This book helps you use that experience to build, evaluate and run AI systems. Start with a working baseline and keep asking what your results really support.
Check your starting point
Try these without an assistant writing the first attempt. Documentation is fine. This is a self-check, not a timed exam.
- Read a CSV with Python, count missing values, and write a function with one meaningful test.
- Make a Git commit and explain what it includes and excludes.
- Call an HTTP endpoint, inspect a failed response, and keep credentials out of commits.
- Explain the difference between a percentage and a percentage-point change: from 20% to 25% is 5 percentage points and a 25% relative increase.
- Explain why
(100, 1) + (100,)can create an unexpected array. If this is new, Chapter 2 teaches it.
If the first three are new, start with the programming resources in Chapter 1. If only the number work is new, begin the normal path. You can write your first baseline before finishing any mathematics textbook.
Your next useful hour
Create a repository and a learning log. Write one sentence naming a person, a decision and the data that might help: “A support lead wants to spot tickets that need escalating, using information available when the ticket arrives.” Then follow orientation week.
Choose one role to explore while keeping up the shared foundations. The role branches in Chapter 7 explain what to build, what to explain and what to recognise. You do not have to finish every branch.
The book has three reading layers
Core. Follow the chapter plan, work through its example, and try the exercise before opening the answer. Each chapter ends with something you can show.
Practice. Diagnose a failure, compare alternatives, and note the limits of a result. Build on the same project where the domain and data make sense.
Research. Optional notes and annotated resources go deeper. Treat a recent paper as something to understand and test, not as a default production choice.
Website reading times cover reading only. Practice-hour figures are planning estimates for someone who already develops software, not measured completion times.
Two ways to work on the same path
The reference lab gives you synthetic data, reproducible commands, filled-in reports and deliberate failures. Run it locally with Python. Its results illustrate its stated assumptions, not real commercial impact.
Your own project uses data you found and are allowed to use. Start with the dataset checklist in Chapter 2. As soon as you know what you want to predict, set aside a test set, before you look at the outcomes or fit any preprocessing, and leave it alone until the end. If your data cannot support the next claim, make the claim smaller or find better data. The reference lab is always there for practice.
A useful completion standard
Work is finished when you can run it from a clean checkout, explain one decision that mattered, diagnose a plausible failure, and show a result with its limits. Documentation can stay open. An honest, reproducible negative or inconclusive comparison is a finished analysis too.
Read offline
The website includes a complete print edition and an EPUB download. Interactive figures also have static explanations, and the Python lab runs without a model API. Reading progress is saved only in this browser on this device, and you can clear it from the reader controls.