Introduction to Machine Learning with Python
A Guide for Data Scientists
By Andreas C. Müller, Sarah Guido
Publisher: O'Reilly Media
Final Release Date: September 2016
Pages: 392



Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:
• Fundamental concepts and applications of machine learning
• Advantages and shortcomings of widely used machine learning algorithms
• How to represent data processed by machine learning, including which data aspects to focus on
• Advanced methods for model evaluation and parameter tuning
• The concept of pipelines for chaining models and encapsulating your workflow
• Methods for working with text data, including text-specific processing techniques
• Suggestions for improving your machine learning and data science skills

Table of Contents

Chapter 1 Introduction
Why Machine Learning?
Why Python?
scikit-learn
Essential Libraries and Tools
Python 2 Versus Python 3
Versions Used in this Book
A First Application: Classifying Iris Species
Summary and Outlook

Chapter 2 Supervised Learning
Classification and Regression
Generalization, Overfitting, and Underfitting
Supervised Machine Learning Algorithms
Uncertainty Estimates from Classifiers
Summary and Outlook

Chapter 3 Unsupervised Learning and Preprocessing
Types of Unsupervised Learning
Challenges in Unsupervised Learning
Preprocessing and Scaling
Dimensionality Reduction, Feature Extraction, and Manifold Learning
Clustering
Summary and Outlook

Chapter 4 Representing Data and Engineering Features
Categorical Variables
Binning, Discretization, Linear Models, and Trees
Interactions and Polynomials
Univariate Nonlinear Transformations
Automatic Feature Selection
Utilizing Expert Knowledge
Summary and Outlook

Chapter 5 Model Evaluation and Improvement
Cross-Validation
Grid Search
Evaluation Metrics and Scoring
Summary and Outlook

Chapter 6 Algorithm Chains and Pipelines
Parameter Selection with Preprocessing
Building Pipelines
Using Pipelines in Grid Searches
The General Pipeline Interface
Grid-Searching Preprocessing Steps and Model Parameters
Grid-Searching Which Model To Use
Summary and Outlook

Chapter 7 Working with Text Data
Types of Data Represented as Strings
Example Application: Sentiment Analysis of Movie Reviews
Representing Text Data as a Bag of Words
Stopwords
Rescaling the Data with tf–idf
Investigating Model Coefficients
Bag-of-Words with More Than One Word (n-Grams)
Advanced Tokenization, Stemming, and Lemmatization
Topic Modeling and Document Clustering
Summary and Outlook

Chapter 8 Wrapping Up
Approaching a Machine Learning Problem
From Prototype to Production
Testing Production Systems
Building Your Own Estimator
Where to Go from Here
Conclusion