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Mastering Machine Learning Algorithms

3.4 (5)

Mastering Machine Learning Algorithms

3.4 (5)

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.

Preface

Preface

Who this book is for

What this book covers

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Free Chapter

Machine Learning Model Fundamentals

Machine Learning Model Fundamentals

Models and data

Features of a machine learning model

Loss and cost functions

Summary

Introduction to Semi-Supervised Learning

Introduction to Semi-Supervised Learning

Semi-supervised scenario

Generative Gaussian mixtures

Contrastive pessimistic likelihood estimation

Semi-supervised Support Vector Machines (S3VM)

Transductive Support Vector Machines (TSVM)

Summary

Graph-Based Semi-Supervised Learning

Graph-Based Semi-Supervised Learning

Label propagation

Label spreading

Label propagation based on Markov random walks

Manifold learning

Summary

Bayesian Networks and Hidden Markov Models

Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem

Bayesian networks

Hidden Markov Models (HMMs)

Summary

EM Algorithm and Applications

EM Algorithm and Applications

MLE and MAP learning

EM algorithm

Gaussian mixture

Factor analysis

Principal Component Analysis

Independent component analysis

Addendum to HMMs

Summary

Hebbian Learning and Self-Organizing Maps

Hebbian Learning and Self-Organizing Maps

Hebb's rule

Sanger's network

Rubner-Tavan's network

Self-organizing maps

Summary

Clustering Algorithms

Clustering Algorithms

k-Nearest Neighbors

K-means

Fuzzy C-means

Spectral clustering

Summary

Ensemble Learning

Ensemble Learning

Ensemble learning fundamentals

Random forests

AdaBoost

Gradient boosting

Ensembles of voting classifiers

Ensemble learning as model selection

Summary

Neural Networks for Machine Learning

Neural Networks for Machine Learning

The basic artificial neuron

Perceptron

Multilayer perceptrons

Optimization algorithms

Regularization and dropout

Batch normalization

Summary

Advanced Neural Models

Advanced Neural Models

Deep convolutional networks

Recurrent networks

Transfer learning

Summary

Autoencoders

Autoencoders

Autoencoders

Variational autoencoders

Summary

Generative Adversarial Networks

Generative Adversarial Networks

Adversarial training

Wasserstein GAN (WGAN)

Summary

Deep Belief Networks

Deep Belief Networks

MRF

RBMs

DBNs

Summary

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

Reinforcement Learning fundamentals

Policy iteration

Value iteration

TD(0) algorithm

Summary

Advanced Policy Estimation Algorithms

Advanced Policy Estimation Algorithms

TD(λ) algorithm

SARSA algorithm

Q-learning

Summary

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Semi-supervised Support Vector Machines (S3VM)

When we discussed the cluster assumption, we also defined the low-density regions as boundaries and the corresponding problem as low-density separation. A common supervised classifier which is based on this concept is a Support Vector Machine (SVM), the objective of which is to maximize the distance between the dense regions where the samples must be. For a complete description of linear and kernel-based SVMs, please refer to Bonaccorso G., Machine Learning Algorithms, Packt Publishing; however, it's useful to remind yourself of the basic model for a linear SVM with slack variables ξ_i:

This model is based on the assumptions that y_i can be either -1 or 1. The slack variables ξ_i or soft-margins are variables, one for each sample, introduced to reduce the strength imposed by the original condition...

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