Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
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
1.
Giving Computers
the Ability to Learn from Data
2.
Training Simple ML
Algorithms for Classification
3.
ML Classifiers
Using scikit-learn
4.
Building Good
Training Datasets - Data Preprocessing
5.
Compressing Data
via Dimensionality Reduction
6.
Best Practices for
Model Evaluation and Hyperparameter Tuning
7.
Combining Different
Models for Ensemble Learning
8.
Applying ML to
Sentiment Analysis
9.
Embedding a ML
Model into a Web Application
10.
Predicting
Continuous Target Variables with Regression Analysis
11.
Working with
Unlabeled Data - Clustering Analysis
12.
Implementing
Multilayer Artificial Neural Networks
13.
Parallelizing
Neural Network Training with TensorFlow
14.
TensorFlow
Mechanics
15.
Classifying Images
with Deep Convolutional Neural Networks
16.
Modeling Sequential
Data Using Recurrent Neural Networks
17.
GANs for
Synthesizing New Data
18.
RL for Decision
Making in Complex Environments
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