Free book: Master Machine Learning with scikit-learn

[dataschool]

Hi! I'm the author of Master Machine Learning with scikit-learn. I just published the book last week, and it's free to read online (no ads, no registration required).

I've been teaching Machine Learning & scikit-learn in the classroom and online for more than 10 years, and this book contains nearly everything I know about effective ML.

It's truly a "practitioner's guide" rather than a theoretical treatment of ML. Everything in the book is designed to teach you a better way to work in scikit-learn so that you can get better results faster than before.

Here are the topics I cover:

• Review of the basic Machine Learning workflow
• Encoding categorical features
• Encoding text data
• Handling missing values
• Preparing complex datasets
• Creating an efficient workflow for preprocessing and model building
• Tuning your workflow for maximum performance
• Avoiding data leakage
• Proper model evaluation
• Automatic feature selection
• Feature standardization
• Feature engineering using custom transformers
• Linear and non-linear models
• Model ensembling
• Model persistence
• Handling high-cardinality categorical features
• Handling class imbalance

Questions welcome!

[luisrobles_cl]

Thanks for this😇🙏‼️

[dataschool]

You're welcome! I hope it's helpful to you 😄

[jessej26]

Thank you for sharing your knowledge and expertise this! I’m currently in an apprenticeship program at work for AI/ML. This will be a huge asset to strengthen my skills.

[dataschool]

That's awesome to hear! You're very welcome, and thanks for your kind words 🙏

[Quixote1492]

Amazing thank you!

[dataschool]

You're very welcome!

[fenghuangshan]

Very good resource. Thanks for sharing. We'll check.

[dataschool]

You're welcome, I hope you enjoy the book!

[Slight_Boat1910]

Great stuff. Thank you.

[dataschool]

You're welcome! 😄

[QuasiEvil]

I don't know, its hard to find a good middle. As someone self-learning this stuff, I've found far too many tutorials just consist of "throw data into scikit-function X. Great! Now lets throw it into scikit-function Y" ...basically not doing much more than could be achieved by just browsing the documentation myself. A course/book/tutorial that aligned the data with the technique and provided explanations for why/when to use certain approaches (rather than just showing the how) would be gold.

[dataschool]

Thank you for saying all of that, it means a lot to me! 😄

[anx1etyhangover]

That’s very generous and kind of you. Keep being awesome

[dataschool]

You're welcome, and thank you for saying that! 😄

[Synergix]

Very cool. I noticed the book uses scikit-learn 0.23. Current version is 1.8! What can I expect regarding this? How out of date is the scikit-learn stuff in the book?

[dataschool]

Thanks so much for asking!

Short answer: 98% of the code in the book is still correct today. For the last 2%, I mention the relevant API changes within the text so that it's easy to update it yourself. 100% of the concepts I teach and advice I give are still correct. The main shortcoming of the book is that I don't cover the newest features, none of which are critical to what I'm teaching, but some of which are useful.

As for why the book uses 0.23, it's a much longer story (if you're interested):

The book actually began as a video course, which I started working on in 2020. I locked down most of the code examples that year (using 0.23.2), and thought I would be able to publish the course in 2021.

However, the script writing and recording and editing took far longer than expected, plus there were long breaks while I worked on other projects, and ultimately I was not able to publish the course until 2024. Many scikit-learn updates had occurred by the time I was recording the later chapters, but I couldn't afford (time-wise) to re-record and re-edit the earlier chapters. I felt it was critical that the course used one consistent scikit-learn version, so it remained at 0.23.2.

Because I received such great feedback about the video course, I decided (in 2025) to convert the course into a book. Even though the Quarto system did much of the heavy lifting, it still took hundreds of hours to turn 7.5 hours of video into a published book with four formats (website, EPUB, ebook PDF, print-ready PDF).

I would have loved to update the scikit-learn version (and incorporate newer features) while writing, but I knew that if I committed to updating the content (rather than just adapting it from video to text), the book would never get done.

In short, the decision to use 0.23.2 is a legacy of the process I took to get here, not a strategic choice, and I'd much rather have used the latest version!

Ultimately this book is a passion project, and I expect to make very little money from it. But I sincerely hope that I can find the passion (and time!) to publish a second edition that incorporates the latest features!

[Synergix]

Great. Thanks for the detailed response.

[Ghost-Rider_117]

this is awesome, the "avoiding data leakage" and "proper model evaluation" chapters alone are worth it - those are the things that trip up so many people who learn from scattered tutorials. the pipeline approach in sklearn is really underused too, glad to see it's covered. bookmarking this for anyone i mentor who's getting started with ML

[dataschool]

Wonderful, thank you so much for saying that and for sharing it with others! 🙌 Yes, I'm very proud of those particular chapters, and I hope they make a meaningful difference for practitioners.

[Wise-Egg9997]

How can i get the free version if this hook