更新时间：2021-07-16 18:01:50
封面
Title Page
Copyright and Credits
Machine Learning Algorithms Second Edition
Dedication
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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
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Download the example code files
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Conventions used
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Reviews
A Gentle Introduction to Machine Learning
Introduction – classic and adaptive machines
Descriptive analysis
Predictive analysis
Only learning matters
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Computational neuroscience
Beyond machine learning – deep learning and bio-inspired adaptive systems
Machine learning and big data
Summary
Important Elements in Machine Learning
Data formats
Multiclass strategies
One-vs-all
One-vs-one
Learnability
Underfitting and overfitting
Error measures and cost functions
PAC learning
Introduction to statistical learning concepts
MAP learning
Maximum likelihood learning
Class balancing
Resampling with replacement
SMOTE resampling
Elements of information theory
Entropy
Cross-entropy and mutual information
Divergence measures between two probability distributions
Feature Selection and Feature Engineering
scikit-learn toy datasets
Creating training and test sets
Managing categorical data
Managing missing features
Data scaling and normalization
Whitening
Feature selection and filtering
Principal Component Analysis
Non-Negative Matrix Factorization
Sparse PCA
Kernel PCA
Independent Component Analysis
Atom extraction and dictionary learning
Visualizing high-dimensional datasets using t-SNE
Regression Algorithms
Linear models for regression
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
R2 score
Explained variance
Regressor analytic expression
Ridge Lasso and ElasticNet
Ridge
Lasso
ElasticNet
Robust regression
RANSAC
Huber regression
Bayesian regression
Polynomial regression
Isotonic regression
Linear Classification Algorithms
Linear classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Passive-aggressive algorithms
Passive-aggressive regression
Finding the optimal hyperparameters through a grid search
Classification metrics
Confusion matrix
Precision
Recall
