Book description
Unlock deeper insights into Machine Leaning with this vital guide to cuttingedge predictive analytics
About This Book
 Leverage Python’s most powerful opensource libraries for deep learning, data wrangling, and data visualization
 Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
 Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets
Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
What You Will Learn
 Explore how to use different machine learning models to ask different questions of your data
 Learn how to build neural networks using Pylearn 2 and Theano
 Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
 Discover how to embed your machine learning model in a web application for increased accessibility
 Predict continuous target outcomes using regression analysis
 Uncover hidden patterns and structures in data with clustering
 Organize data using effective preprocessing techniques
 Get to grips with sentiment analysis to delve deeper into textual and social media data
In Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikitlearn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Table of contents

Python Machine Learning
 Table of Contents
 Python Machine Learning
 Credits
 Foreword
 About the Author
 About the Reviewers
 www.PacktPub.com
 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
 An introduction to the basic terminology and notations
 A roadmap for building machine learning systems
 Using Python for machine learning
 Summary
 2. Training Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using Scikitlearn
 Choosing a classification algorithm
 First steps with scikitlearn
 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 a simple linear regression model
 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
 Summary
 11. Working with Unlabeled Data – Clustering Analysis

12. Training Artificial Neural Networks for Image Recognition
 Modeling complex functions with artificial neural networks
 Classifying handwritten digits
 Training an artificial neural network
 Developing your intuition for backpropagation
 Debugging neural networks with gradient checking
 Convergence in neural networks
 Other neural network architectures
 A few last words about neural network implementation
 Summary
 13. Parallelizing Neural Network Training with Theano
 Index
Product information
 Title: Python Machine Learning
 Author(s):
 Release date: September 2015
 Publisher(s): Packt Publishing
 ISBN: 9781783555130
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