Cover

Copyright

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: 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

Chapter 2: 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

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

Feature selection and filtering

Principal component analysis

Non-negative matrix factorization

Sparse PCA

Kernel PCA

Atom extraction and dictionary learning

References

Summary

Chapter 4: 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

Chapter 5: 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

Chapter 6: Naive Bayes

Bayes' theorem

Naive Bayes classifiers

Naive Bayes in scikit-learn

Bernoulli naive Bayes

Multinomial naive Bayes

Gaussian naive Bayes

References

Summary

Chapter 7: 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

Chapter 8: 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

Chapter 9: 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

Chapter 10: Hierarchical Clustering

Hierarchical strategies

Agglomerative clustering

Dendrograms

Agglomerative clustering in scikit-learn

Connectivity constraints

References

Summary

Chapter 11: 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

Chapter 12: 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

Chapter 13: 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

Chapter 14: 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

Chapter 15: 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

Index