<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.
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<aside> 🚨 I've noticed that taking notes on this site while reading the book significantly extends the time it takes to finish the book. I've stopped noting everything, as in previous chapters, and instead continue reading by highlighting/hand-writing notes instead. I plan to return to the detailed style when I have more time.
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<aside> ✊ This book contains 1007 pages of readable content. If you read at a pace of 10 pages per day, it will take you approximately 3.3 months (without missing a day) to finish it. If you aim to complete it in 2 months, you'll need to read at least 17 pages per day.
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This book is organized in 2 parts:
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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A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. — Tom Mitchell, 1997.
Example: email spam filter ← give it examples of spam/non-spam emails so that it can learn to flag spam.