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.

We provide links for the available notebooks:

• The Machine Learning Landscape
• End-to-End Machine Learning Project
• Classification
• Training Models
• Support Vector Machines
• Decision Trees
• Ensemble Learning and Random Forests
• Dimensionality Reduction
• Unsupervised Learning Techniques