Hands-on ML with Scikit-Learn, Keras & TF by Aurelien Geron
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-LearnKerasTensorFlow
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
- End-to-End Machine Learning Project
- Classification
- Training Models
- Support Vector Machines
- Decision Trees
- Ensemble Learning and Random Forests
- Dimensionality Reduction
- Unsupervised Learning Techniques