Machine Learning Algorithms（Second Edition）

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Summary

Feature unions

Pipelines

Scikit-learn tools for machine learning architectures

A brief introduction to distributed architectures

GPU support

Visualization

Modeling/grid search/cross-validation

Data conversion

Data augmentation

Dimensionality reduction

Normalization and regularization

Data collection

Machine learning architectures

Creating a Machine Learning Architecture

Summary

Image convolution

Classification with a multilayer perceptron

Logistic regression

Computing gradients

A brief introduction to TensorFlow

An example of an LSTM network with Keras

An example of a deep convolutional network with Keras

Recurrent Neural Networks

Batch normalization layers

Dropout layers

Convolutional layers

Fully connected layers

Deep model layers

Advanced Deep Learning Models

Summary

Interfacing Keras to scikit-learn

MLPs with Keras

Artificial neural networks

Deep learning at a glance

Introducing Neural Networks

Summary

VADER sentiment analysis with NLTK

Sentiment analysis

Introducing Word2vec with Gensim

Latent Dirichlet Allocation

Probabilistic Latent Semantic Analysis

Latent Semantic Analysis

Topic modeling

Topic Modeling and Sentiment Analysis in NLP

Summary

A sample text classifier based on the Reuters corpus

Named Entity Recognition

Part-of-Speech

TF-IDF vectorizing

N-grams

Count vectorizing

Vectorizing

Stemming

Language detection

Stopword removal

Word tokenizing

Sentence tokenizing

Tokenizing

The Bag-of-Words strategy

Corpora examples

NLTK and built-in corpora

Introducing Natural Language Processing

Summary

ALS with Apache Spark MLlib

Alternating least squares strategy

Singular value decomposition strategy

Model-based collaborative filtering

Model-free (or memory-based) collaborative filtering

Content-based systems

Implementing a user-based system with scikit-learn

Naive user-based systems

Introducing Recommendation Systems

Summary

Connectivity constraints

Agglomerative Clustering in scikit-learn

Dendrograms

Agglomerative Clustering

Hierarchical strategies

Hierarchical Clustering

Summary

Biclustering

BIRCH

Mini-batch K-means

Online Clustering

Spectral Clustering

DBSCAN

Advanced Clustering

Summary

Adjusted Rand Index

Completeness

Homogeneity

Evaluation methods based on the ground truth

Cluster instability

Calinski-Harabasz index

Silhouette score

Optimizing the inertia

Finding the optimal number of clusters

K-means

Finding the optimal number of components

Gaussian mixture

k-NN

Clustering basics

Clustering Fundamentals

Summary

Voting classifier

Gradient Tree Boosting

AdaBoost

Feature importance in Random Forests

Random Forests

Introduction to Ensemble Learning

Example of Decision Tree regression with the Concrete Compressive Strength dataset

Decision Tree regression

Decision Tree classification with scikit-learn

Feature importance

Misclassification impurity index

Cross-entropy impurity index

Gini impurity index

Impurity measures

Binary decisions

Binary Decision Trees

Decision Trees and Ensemble Learning

Summary

Introducing semi-supervised Support Vector Machines (S3VM)

An example of SVR with the Airfoil Self-Noise dataset

Support Vector Regression

ν-Support Vector Machines

Non-linear examples

Custom kernels

Sigmoid kernel

Polynomial kernel

Radial Basis Function

Kernel-based classification

Linear classification

SVMs with scikit-learn

Linear SVM

Support Vector Machines

Summary

Discriminant analysis

Gaussian Naive Bayes

An example of Multinomial Naive Bayes for text classification

Multinomial Naive Bayes

Bernoulli Naive Bayes

Naive Bayes in scikit-learn

Naive Bayes classifiers

Bayes' theorem

Naive Bayes and Discriminant Analysis

Summary

ROC curve

Learning curve

Global classification report

Cohen's Kappa

F-Beta

Recall

Precision

Confusion matrix

Classification metrics

Finding the optimal hyperparameters through a grid search

Passive-aggressive regression

Passive-aggressive algorithms

Stochastic gradient descent algorithms

Implementation and optimizations

Logistic regression

Linear classification

Linear Classification Algorithms

Summary

Isotonic regression

Polynomial regression

Bayesian regression

Huber regression

RANSAC

Robust regression

ElasticNet

Lasso

Ridge

Ridge Lasso and ElasticNet

Regressor analytic expression

Explained variance

R2 score

Linear regression with scikit-learn and higher dimensionality

A bidimensional example

Linear models for regression

Regression Algorithms

Summary

Visualizing high-dimensional datasets using t-SNE

Atom extraction and dictionary learning

Independent Component Analysis

Kernel PCA

Sparse PCA

Non-Negative Matrix Factorization

Principal Component Analysis

Feature selection and filtering

Whitening

Data scaling and normalization

Managing missing features

Managing categorical data

Creating training and test sets

scikit-learn toy datasets

Feature Selection and Feature Engineering

Summary

Divergence measures between two probability distributions

Cross-entropy and mutual information

Entropy

Elements of information theory

SMOTE resampling

Resampling with replacement

Class balancing

Maximum likelihood learning

MAP learning

Introduction to statistical learning concepts

PAC learning

Error measures and cost functions

Underfitting and overfitting

Learnability

One-vs-one

One-vs-all

Multiclass strategies

Data formats

Important Elements in Machine Learning

Summary

Machine learning and big data

Beyond machine learning – deep learning and bio-inspired adaptive systems

Computational neuroscience

Reinforcement learning

Semi-supervised learning

Unsupervised learning

Supervised learning

Only learning matters

Predictive analysis

Descriptive analysis

Introduction – classic and adaptive machines

A Gentle Introduction to Machine Learning

Reviews

Get in touch

Conventions used

Download the color images

Download the example code files

To get the most out of this book

What this book covers

Who this book is for

Preface

Packt is searching for authors like you

About the reviewer

About the author

Contributors

PacktPub.com

Why subscribe?

Packt Upsell

Dedication

Machine Learning Algorithms Second Edition

Copyright and Credits

Title Page

封面

封面

Title Page

Copyright and Credits

Machine Learning Algorithms Second Edition

Dedication

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the author

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

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

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

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

Summary

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

Summary

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

F-Beta

Cohen's Kappa

Global classification report

Learning curve

ROC curve

Summary

Naive Bayes and Discriminant Analysis

Bayes' theorem

Naive Bayes classifiers

Naive Bayes in scikit-learn

Bernoulli Naive Bayes

Multinomial Naive Bayes

An example of Multinomial Naive Bayes for text classification

Gaussian Naive Bayes

Discriminant analysis

Summary

Support Vector Machines

Linear SVM

SVMs with scikit-learn

Linear classification

Kernel-based classification

Radial Basis Function

Polynomial kernel

Sigmoid kernel

Custom kernels

Non-linear examples

ν-Support Vector Machines

Support Vector Regression

An example of SVR with the Airfoil Self-Noise dataset

Introducing semi-supervised Support Vector Machines (S3VM)

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

Decision Tree regression

Example of Decision Tree regression with the Concrete Compressive Strength dataset

Introduction to Ensemble Learning

Random Forests

Feature importance in Random Forests

AdaBoost

Gradient Tree Boosting

Voting classifier

Summary

Clustering Fundamentals

Clustering basics

k-NN

Gaussian mixture

Finding the optimal number of components

K-means

Finding the optimal number of clusters

Optimizing the inertia

Silhouette score

Calinski-Harabasz index

Cluster instability

Evaluation methods based on the ground truth

Homogeneity

Completeness

Adjusted Rand Index

Summary

Advanced Clustering

DBSCAN

Spectral Clustering

Online Clustering

Mini-batch K-means

BIRCH

Biclustering

Summary

Hierarchical Clustering

Hierarchical strategies

Agglomerative Clustering

Dendrograms

Agglomerative Clustering in scikit-learn

Connectivity constraints

Summary

Introducing Recommendation Systems

Naive user-based systems

Implementing a user-based system 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

ALS with Apache Spark MLlib

Summary

Introducing 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

Part-of-Speech

Named Entity Recognition

A sample text classifier based on the Reuters corpus

Summary

Topic Modeling and Sentiment Analysis in NLP

Topic modeling

Latent Semantic Analysis

Probabilistic Latent Semantic Analysis

Latent Dirichlet Allocation

Introducing Word2vec with Gensim

Sentiment analysis

VADER sentiment analysis with NLTK

Summary

Introducing Neural Networks

Deep learning at a glance

Artificial neural networks

MLPs with Keras

Interfacing Keras to scikit-learn

Summary

Advanced Deep Learning Models

Deep model layers

Fully connected layers

Convolutional layers

Dropout layers

Batch normalization layers

Recurrent Neural Networks

An example of a deep convolutional network with Keras

An example of an LSTM network with Keras

A brief introduction to TensorFlow

Computing gradients

Logistic regression

Classification with a multilayer perceptron

Image convolution

Summary

Creating a Machine Learning Architecture

Machine learning architectures

Data collection

Normalization and regularization

Dimensionality reduction

Data augmentation

Data conversion

Modeling/grid search/cross-validation

Visualization

GPU support

A brief introduction to distributed architectures

Scikit-learn tools for machine learning architectures

Pipelines

Feature unions

Summary

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