Machine Learning Algorithms by Bonaccorso, Giuseppe -- Read -- Imperial Library of Trantor

Index

Title Page Copyright

Machine Learning Algorithms

Credits About the Author About the Reviewers www.PacktPub.com

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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

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

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