Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow

Why we included this project

This repository collects the Jupyter notebooks that accompany Part I of Aurélien Géron's Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. It covers the scikit-learn portion of the book: classification, training models, support vector machines, decision trees, ensembles, dimensionality reduction, and unsupervised learning. Each notebook pairs short explanations with runnable code, so you can follow along, change parameters, and see how the model responds. The order matches the book's chapters, which makes it a handy study companion when you're working through the material or need a quick refresher on a specific technique before applying it yourself. It's learning material, not production software, but for building a solid foundation in classic ML it does the job.