Hands-On Machine Learning with Scikit-Learn and TensorFlow (Python)

This book is most widely used to learn machine learning with Scikit-Learn and TensorFlow. The book gives more general examples for understanding machine learning and deep learning. The hands-on machine learning book is to use the TensorFlow and Scikit-learn library of python to train a machine. All the example data available exercise at GitHub.
This book has two editions (First Edition, Second edition). Both editions have used to learn machine learning and deep learning. The first edition released in 2017 and the Second edition is release in 2019 with updated.
You can buy kindle book of the second edition and paperback.

In the second edition, TensorFlow 2 is used. While the first edition used the old version of TensorFlow but the concept of training is the same.

You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

1. Explore the machine learning landscape, particularly neural nets.
2. Use Scikit-learn to track an example machine-learning project end-to-end.
3. Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods.
4. Use the TensorFlow library to build and train neural nets.
5. Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning.
6. Learn techniques for training and scaling deep neural nets.
7. Apply practical code examples without acquiring excessive machine learning theory or algorithm details.

Both editions are so good for beginners who start learning about machine learning and deep learning.