本书内容包括：研究特征提取与特征工程过程、评估线性回归的性能和误差估计、使用不同类型的算法构建数据模型并理解其工作原理、调整支持向量机(SVM)的参数、探讨自然语言处理(NLP)和推荐系统的概念、从头开始创建一个机器学习架构。
Preface
Chapter 1: 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
Chapter 2: 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
Summary
Chapter 3: 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
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