更新时间：2021-07-02 18:54:04
coverpage
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Why subscribe?
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
A Gentle Introduction to Machine Learning
Introduction - classic and adaptive machines
Only learning matters
Supervised learning
Unsupervised learning
Reinforcement learning
Beyond machine learning - deep learning and bio-inspired adaptive systems
Machine learning and big data
Further reading
Summary
Important Elements in Machine Learning
Data formats
Multiclass strategies
One-vs-all
One-vs-one
Learnability
Underfitting and overfitting
Error measures
PAC learning
Statistical learning approaches
MAP learning
Maximum-likelihood learning
Elements of information theory
References
Feature Selection and Feature Engineering
scikit-learn toy datasets
Creating training and test sets
Managing categorical data
Managing missing features
Data scaling and normalization
Feature selection and filtering
Principal component analysis
Non-negative matrix factorization
Sparse PCA
Kernel PCA
Atom extraction and dictionary learning
Linear Regression
Linear models
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
Regressor analytic expression
Ridge Lasso and ElasticNet
Robust regression with random sample consensus
Polynomial regression
Isotonic regression
Logistic Regression
Linear classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Finding the optimal hyperparameters through grid search
Classification metrics
ROC curve
Naive Bayes
Bayes' theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
Bernoulli naive Bayes
Multinomial naive Bayes
Gaussian naive Bayes
Support Vector Machines
Linear support vector machines
scikit-learn implementation
Kernel-based classification
Radial Basis Function
Polynomial kernel
Sigmoid kernel
Custom kernels
Non-linear examples
Controlled support vector machines
Support vector regression
