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Book details

File Size: 94 MB

Format: epub,mobi,pdf

Print Length: 770 pages

Publisher: Packt Publishing; 3 edition (December 12, 2019)

Publication Date: December 16, 2019

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B07VBLX2W7



Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.



Key Features

Third edition of the bestselling, widely acclaimed Python machine learning book

Clear and intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices



Book Description

Python Machine Learning, Third Edition 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 all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.



Updated for TensorFlow 2.0, this new third 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 a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.



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 image classification, sentiment analysis, intelligent web applications, and more

Build and train neural networks, GANs, and other models

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis



Who This Book Is For

If 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 Contents

Giving Computers the Ability to Learn from Data

Training Simple ML Algorithms for Classification

ML Classifiers Using scikit-learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying ML to Sentiment Analysis

Embedding a ML Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Implementing Multilayer Artificial Neural Networks

Parallelizing Neural Network Training with TensorFlow

TensorFlow Mechanics

Classifying Images with Deep Convolutional Neural Networks

Modeling Sequential Data Using Recurrent Neural Networks

GANs for Synthesizing New Data

RL for Decision Making in Complex Environments