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Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron
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| 2026-07-31 | 1051 |
| 2026-08-06 | 1054 |
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# Hands-on ML with Scikit-Learn, Keras & TF by Aurelien Geron <div style="text-align:center"><img style="width:100%" src="static/imgs/book_cover.png"></div> This repo is home to notes & code that accompanies Part 1 of Aurelien Geron's "Hands-on ML with Scikit-Learn, Keras & TF" book. The book provides a comprehensive overview of data science, machine learning (with `scikit-learn`), and deep learning (with `tensorflow`). The Book assumes you know close to nothing about machine learning. It uses production-ready Python frameworks such as: - `Scikit-Learn` - `Keras` - `TensorFlow` The author favors a hands-on approach through a series of working examples and just a little bit of theory. Prerequesites: - Some Python programming experience - Familiarity with NumPy, Pandas, and Matplotlib - A reasonable understanding of college-level math (calculus, probability, Linear Algebra, and statistics) The first part of the book is mostly based on `Scikit-Learn`, while the 2nd part is using `Keras/TensorFlow`. ## Roadmap ### The Fundamentals of Machine Learning We provide links for the available notebooks: - [The Machine Learning Landscape](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/01.ML_Landscape.ipynb) - [End-to-End Machine Learning Project](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/02.End-to-End-ML-Project.ipynb) - [Classification](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/03.Classification.ipynb) - [Training Models](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/04.Training-Models.ipynb) - [Support Vector Machines](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/05.SVMs.ipynb) - [Decision Trees](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/06.Decision_Trees.ipynb) - [Ensemble Learning and Random Forests](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/07.Ensembles_RFs.ipynb) - [Dimensionality Reduction](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/08.Dim_Reduction.ipynb) - [Unsupervised Learning Techniques](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/09.Unsupervised_learning.ipynb) ---
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