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Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python
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This repository contains notes and exercises solutions for the following books:
If you are looking to become an expert, check out my books:
They are all extremely comprehensive and offer lots of exercises with detailed solutions.
Selected from shared topics, language and repository description—not editorial ratings.
The GitHub repository for the book "Python Machine Learning: A Beginner's Guide to Scikit-Learn" contains all the code examples discussed in the book. The code is organized by chapter and can be easily accessed and run using Jupyter notebooks. Readers are free to use the code for their own projects and experimentation.
51/100 healthThis jupyter notebook is part of the lecture "Data Science for Raman spectroscopy" imparted by Nicolás Coca-López during the Hands-on Workshop Machine Learning with Python, organized by the B-Phot Student Chapter the 10th of December, 2022
47/100 healthmarcoemorais /
Jupyter notebooks with answers to end of chapter review questions for the textbook Scientific Computing by Michael Heath (2018).
34/100 healthThe Ultimate Machine Learning Roadmap: From Scratch to Production. 📚 Chapter-by-chapter Jupyter Notebooks, Real-World Projects, and Deep Learning internals. 🤖 Master Scikit-Learn, TensorFlow, and PyTorch step-by-step. 🌟 Star to save your progress!
74/100 healthJanMarcelKezmann /
This Repository contains Notes in form of Jupyter Notebooks for every Chapter of the Book "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson
38/100 healthnelsonic1 /
Jupyter Notebooks for each chapter in Spark - The Definitive Guide by Bill Chambers and Matei Zaharia
27/100 health