Python Machine Learning Second
Edition
Table of Contents
Python Machine Learning Second Edition
Credits
About the Authors
About the Reviewers
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Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Giving Computers the Ability to Learn from Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Making predictions about the future with supervised learning
Classification for predicting class labels
Regression for predicting continuous outcomes
Solving interactive problems with reinforcement learning
Discovering hidden structures with unsupervised learning
Finding subgroups with clustering
Dimensionality reduction for data compression
Introduction to the basic terminology and notations
A roadmap for building machine learning systems
Preprocessing – getting data into shape
Training and selecting a predictive model
Evaluating models and predicting unseen data instances
Using Python for machine learning
Installing Python and packages from the Python Package Index
Using the Anaconda Python distribution and package manager
Packages for scientific computing, data science, and machine learning
Summary
2. Training Simple Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
The formal definition of an artificial neuron
The perceptron learning rule
Implementing a perceptron learning algorithm in Python
An object-oriented perceptron API
Training a perceptron model on the Iris dataset
Adaptive linear neurons and the convergence of learning
Minimizing cost functions with gradient descent
Implementing Adaline in Python
Improving gradient descent through feature scaling
Large-scale machine learning and stochastic gradient descent
Summary
3. A Tour of Machine Learning Classifiers Using scikit-learn
Choosing a classification algorithm
First steps with scikit-learn – training a perceptron
Modeling class probabilities via logistic regression
Logistic regression intuition and conditional probabilities
Learning the weights of the logistic cost function
Converting an Adaline implementation into an algorithm for logistic regression
Training a logistic regression model with scikit-learn
Tackling overfitting via regularization
Maximum margin classification with support vector machines
Maximum margin intuition
Dealing with a nonlinearly separable case using slack variables
Alternative implementations in scikit-learn
Solving nonlinear problems using a kernel SVM
Kernel methods for linearly inseparable data
Using the kernel trick to find separating hyperplanes in high-dimensional space
Decision tree learning
Maximizing information gain – getting the most bang for your buck
Building a decision tree
Combining multiple decision trees via random forests
K-nearest neighbors – a lazy learning algorithm
Summary
4. Building Good Training Sets – Data Preprocessing
Dealing with missing data
Identifying missing values in tabular data
Eliminating samples or features with missing values
Imputing missing values
Understanding the scikit-learn estimator API
Handling categorical data
Nominal and ordinal features
Creating an example dataset
Mapping ordinal features
Encoding class labels
Performing one-hot encoding on nominal features
Partitioning a dataset into separate training and test sets
Bringing features onto the same scale
Selecting meaningful features
L1 and L2 regularization as penalties against model complexity
A geometric interpretation of L2 regularization
Sparse solutions with L1 regularization
Sequential feature selection algorithms
Assessing feature importance with random forests
Summary
5. Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
The main steps behind principal component analysis
Extracting the principal components step by step
Total and explained variance
Feature transformation
Principal component analysis in scikit-learn
Supervised data compression via linear discriminant analysis
Principal component analysis versus linear discriminant analysis
The inner workings of linear discriminant analysis
Computing the scatter matrices
Selecting linear discriminants for the new feature subspace
Projecting samples onto the new feature space
LDA via scikit-learn
