Books Recommendation: Hands-On Machine Learning with Scikit-Learn and TensorFlow

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

MAIN CONTENTS

� The Machine Learning Landscape

� What Is Machine Learning?

� Why Use Machine Learning?

� Types of Machine Learning Systems

� Main Challenges of Machine Learning

� End-to-End Machine Learning Project

� Prepare the Data for Machine Learning Algorithms

� Neural Networks and Deep Learning

� Up and Running with TensorFlow

� Introduction to Artificial Neural Networks

� Training Deep Neural Nets

� Distributing TensorFlow Across Devices and Servers

� Convolutional Neural Networks

� Recurrent Neural Networks

� Autoencoders