Book description
Unlock modern machine learning and deep learning techniques with Python by using the latest cuttingedge open source Python libraries.
About This Book
 Second edition of the bestselling book on Machine Learning
 A practical approach to key frameworks in data science, machine learning, and deep learning
 Use the most powerful Python libraries to implement machine learning and deep learning
 Get to know the best practices to improve and optimize your machine learning systems and algorithms
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 and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.
What You Will Learn
 Understand the key frameworks in data science, machine learning, and deep learning
 Harness the power of the latest Python open source libraries in machine learning
 Explore machine learning techniques using challenging realworld data
 Master deep neural network implementation using the TensorFlow library
 Learn the mechanics of classification algorithms to implement the best tool for the job
 Predict continuous target outcomes using regression analysis
 Uncover hidden patterns and structures in data with clustering
 Delve deeper into textual and social media data using sentiment analysis
In Detail
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikitlearn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world.
If you’ve read the first edition of this book, you’ll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikitlearn.
Style and Approach
Python Machine Learning Second Edition takes a practical, handson coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
Publisher resources
Table of contents

Python Machine Learning Second Edition
 Table of Contents
 Python Machine Learning Second Edition
 Credits
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Packt is Searching for Authors Like You
 Preface

1. Giving Computers the Ability to Learn from Data
 Building intelligent machines to transform data into knowledge
 The three different types of machine learning
 Introduction to the basic terminology and notations
 A roadmap for building machine learning systems
 Using Python for machine learning
 Summary
 2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikitlearn
 Choosing a classification algorithm
 First steps with scikitlearn – training a perceptron
 Modeling class probabilities via logistic regression
 Maximum margin classification with support vector machines
 Solving nonlinear problems using a kernel SVM
 Decision tree learning
 Knearest neighbors – a lazy learning algorithm
 Summary
 4. Building Good Training Sets – Data Preprocessing
 5. Compressing Data via Dimensionality Reduction
 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
 7. Combining Different Models for Ensemble Learning
 8. Applying Machine Learning to Sentiment Analysis
 9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis
 Introducing linear regression
 Exploring the Housing dataset
 Implementing an ordinary least squares linear regression model
 Fitting a robust regression model using RANSAC
 Evaluating the performance of linear regression models
 Using regularized methods for regression
 Turning a linear regression model into a curve – polynomial regression
 Dealing with nonlinear relationships using random forests
 Summary
 11. Working with Unlabeled Data – Clustering Analysis
 12. Implementing a Multilayer Artificial Neural Network from Scratch
 13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow
 Key features of TensorFlow
 TensorFlow ranks and tensors
 Understanding TensorFlow's computation graphs
 Placeholders in TensorFlow
 Variables in TensorFlow
 Building a regression model
 Executing objects in a TensorFlow graph using their names
 Saving and restoring a model in TensorFlow
 Transforming Tensors as multidimensional data arrays
 Utilizing control flow mechanics in building graphs
 Visualizing the graph with TensorBoard
 Summary
 15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks
 Introducing sequential data
 RNNs for modeling sequences
 Implementing a multilayer RNN for sequence modeling in TensorFlow
 Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs
 Project two – implementing an RNN for characterlevel language modeling in TensorFlow
 Chapter and book summary
 Index
Product information
 Title: Python Machine Learning  Second Edition
 Author(s):
 Release date: September 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787125933
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