Machine Learning Algorithms电子书

Title Page

Copyright

Machine Learning Algorithms

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

Summary

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

References

Summary

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

References

Summary

Logistic Regression

Linear classification

Logistic regression

Implementation and optimizations

Stochastic gradient descent algorithms

Finding the optimal hyperparameters through grid search

Classification metrics

ROC curve

Summary

Naive Bayes

Bayes' theorem

Naive Bayes classifiers

Naive Bayes in scikit-learn

Bernoulli naive Bayes

Multinomial naive Bayes

Gaussian naive Bayes

References

Summary

Support Vector Machines

Linear support vector machines

scikit-learn implementation

Linear classification

Kernel-based classification

Radial Basis Function

Polynomial kernel

Sigmoid kernel

Custom kernels

Non-linear examples

Controlled support vector machines

Support vector regression

References

Summary

Decision Trees and Ensemble Learning

Binary decision trees

Binary decisions

Impurity measures

Gini impurity index

Cross-entropy impurity index

Misclassification impurity index

Feature importance

Decision tree classification with scikit-learn

Ensemble learning

Random forests

Feature importance in random forests

AdaBoost

Gradient tree boosting

Voting classifier

References

Summary

Clustering Fundamentals

Clustering basics

K-means

Finding the optimal number of clusters

Optimizing the inertia

Silhouette score

Calinski-Harabasz index

Cluster instability

DBSCAN

Spectral clustering

Evaluation methods based on the ground truth

Homogeneity

Completeness

Adjusted rand index

References

Summary

Hierarchical Clustering

Hierarchical strategies

Agglomerative clustering

Dendrograms

Agglomerative clustering in scikit-learn

Connectivity constraints

References

Summary

Introduction to Recommendation Systems

Naive user-based systems

User-based system implementation with scikit-learn

Content-based systems

Model-free (or memory-based) collaborative filtering

Model-based collaborative filtering

Singular Value Decomposition strategy

Alternating least squares strategy

Alternating least squares with Apache Spark MLlib

References

Summary

Introduction to Natural Language Processing

NLTK and built-in corpora

Corpora examples

The bag-of-words strategy

Tokenizing

Sentence tokenizing

Word tokenizing

Stopword removal

Language detection

Stemming

Vectorizing

Count vectorizing

N-grams

Tf-idf vectorizing

A sample text classifier based on the Reuters corpus

References

Summary

Topic Modeling and Sentiment Analysis in NLP

Topic modeling

Latent semantic analysis

Probabilistic latent semantic analysis

Latent Dirichlet Allocation

Sentiment analysis

VADER sentiment analysis with NLTK

References

Summary

A Brief Introduction to Deep Learning and TensorFlow

Deep learning at a glance

Artificial neural networks

Deep architectures

Fully connected layers

Convolutional layers

Dropout layers

Recurrent neural networks

A brief introduction to TensorFlow

Computing gradients

Logistic regression

Classification with a multi-layer perceptron

Image convolution

A quick glimpse inside Keras

References

Summary

Creating a Machine Learning Architecture

Machine learning architectures

Data collection

Normalization

Dimensionality reduction

Data augmentation

Data conversion

Modeling/Grid search/Cross-validation

Visualization

scikit-learn tools for machine learning architectures

Pipelines

Feature unions

References

Summary