Introduction to Machine Learning with Python

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

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

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

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

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

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

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

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