<aside> ⚠️ This note serves as a reminder of the book's content, including additional research on the mentioned topics. It is not a substitute for the book. Most images are sourced from the book or referenced.

</aside>

👉 List of all notes for this book. IMPORTANT UPDATE November 18, 2024: I've stopped taking detailed notes from the book and now only highlight and annotate directly in the PDF files/book. With so many books to read, I don't have time to type everything. In the future, if I make notes while reading a book, they'll contain only the most notable points (for me).

Information

• These notes are for the version 3.
• Author: Aurélien Géron.
• Official jupyter notebooks (open them in Colab)

Roadmap

This book is organized in 2 parts:

• Part I — The Fundamentals of Machine Learning — Based mostly on Scikit-Learn
• Part II — Neural Networks and Deep Learning — Based on TensorFlow & Keras

Other resources

• Andrew Ng’s ML course on Coursera (my notes for the old version of this course).
• Scikit-Learn’s User Guide.
• Dataquest.
• Blogs listed on Quora.
• Kaggle
• Other introductory books

The chapter 1 introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. If you already familiar with machine learning basics, you may want to skip directly to Chapter 2.

<aside> 📔

Jupyter notebook for this chapter: on Github, on Colab, on Kaggle.

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What Is Machine Learning?