Have you always wanted to learn deep learning but are afraid it'll be too difficult for you? This book is for you.Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.Book DescriptionPython Machine Learning, is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers most of the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, this tutorial book teaches the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow, skit-learn, Keras, and theano, this edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores analysis by giving some examples, helping you learn how to use machine learning algorithms to classify or predict documents output.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn-Master the frameworks, models, and techniques that enable machines to 'learn' from data-Use scikit-learn for machine learning and TensorFlow for deep learning-Apply machine learning to classification, predict predict customer churning, and more-Build and train neural networks, GANs, CNN, and other models-Discover best practices for evaluating and tuning models-Predict target outcomes using optimization algorithm such as Gradient Descent algorithm analysis-Overcome challenges in deep learning algorithms by using dropout, regulation-Who This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.Table of Contents1.Giving Computers the Ability to Learn from Data2.Training Simple ML Algorithms for Classification3.ML Classifiers Using scikit-learn4.Building Good Training Datasets - Data Preprocessing5.Compressing Data via Dimensionality Reduction6.Best Practices for Model Evaluation and Hyperparameter Tuning7.Combining Different Models for Ensemble Learning8.Predicting Continuous Target Variables with supversized learning 9.Implementing Multilayer Artificial Neural Networks10.Modeling Sequential Data Using Recurrent Neural Networks11.GANs for Synthesizing New Data...and so much more....In every chapter, you can edit the examples online