Python Machine Learning: Machine Learning and Deep Learning with Python,scikit-learn,and TensorFlow 2,3rd Edition
Authors: Sebastian Raschka – Vahid Mirjalili
ISBN-10: 1789955750
ISBN-13: 9781789955750
Released: 2019-12-12
Print Length 页数: 770 pages
Publisher finelybook 出版社: Packt
Book Description
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2,GANs,and reinforcement learning.
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
Contents
Preface
Chapter 1: Giving Computers the Ability to Learn from Data
Chapter 2: Training Simple Machine Learning Algorithms for Classification
Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
Chapter 4: Building Good Training Datasets-Data Preprocessing
Chapter 5: Compressing Data via Dimensionality Reduction
Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Chapter 7: Combining Different Models for Ensemble Learning
Chapter 8: Applying Machine Learning to Sentiment Analysis
Chapter 9: Embedding a Machine Learning Model into a Web Application
Chapter 10: Predicting Continuous Target Variables with Regression Analysis
Chapter 11: Working with Unlabeled Data-Clustering Analysis
Chapter 12: Implementing a Multilayer Artificial Neural Network from Scratch
Chapter 13: Parallelizing Neural Network Training with TensorFlow
Chapter 14: Going Deeper-The Mechanics of TensorFlow
Chapter 15: Classifying Images with Deep Convolutional Neural Networks
Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks
Chapter 17: Generative Adversarial Networks for Synthesizing New Data
Chapter 18: Reinforcement Learning for Decision Making in Complex Environments
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Index
