Book Image

Mastering Machine Learning Algorithms - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

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Table of Contents (28 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Machine Learning Model Fundamentals
Machine Learning Model Fundamentals
Models and data
Characteristics of a machine learning model
Summary
Further reading
Free Chapter
2
Loss Functions and Regularization
Loss Functions and Regularization
Defining loss and cost functions
Regularization
Summary
Further reading
3
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Semi-supervised scenario
Generative Gaussian Mixture
Self-Training
Co-Training
Summary
Further reading
4
Advanced Semi-Supervised Classification
Advanced Semi-Supervised Classification
Contrastive Pessimistic Likelihood Estimation
Semi-supervised Support Vector Machines (S3VM)
Transductive Support Vector Machines (TSVM)
Summary
Further reading
5
Graph-Based Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Label propagation
Example of label propagation
Label spreading
Label propagation based on Markov random walks
Manifold learning
Summary
Further reading
6
Clustering and Unsupervised Models
Clustering and Unsupervised Models
K-nearest neighbors
K-means
Evaluation metrics
Summary
Further reading
7
Advanced Clustering and Unsupervised Models
Advanced Clustering and Unsupervised Models
Fuzzy C-means
Spectral clustering
DBSCAN
Summary
Further reading
8
Clustering and Unsupervised Models for Marketing
Clustering and Unsupervised Models for Marketing
Biclustering
Introduction to Market Basket Analysis with the Apriori Algorithm
Summary
Further reading
9
Generalized Linear Models and Regression
Generalized Linear Models and Regression
GLMs
Other regression techniques
Summary
Further reading
10
Introduction to Time-Series Analysis
Introduction to Time-Series Analysis
Time-series
Introduction to linear models for time-series
Summary
Further reading
11
Bayesian Networks and Hidden Markov Models
Bayesian Networks and Hidden Markov Models
Conditional probabilities and Bayes' theorem
Bayesian networks
Hidden Markov Models
Summary
Further reading
12
The EM Algorithm
The EM Algorithm
MLE and MAP Learning
EM Algorithm
Gaussian Mixture
Summary
Further reading
13
Component Analysis and Dimensionality Reduction
Component Analysis and Dimensionality Reduction
Factor Analysis
Principal Component Analysis
Independent Component Analysis
Addendum to Hidden Markov Models
Summary
Further reading
14
Hebbian Learning
Hebbian Learning
Hebb's rule
Sanger's network
Rubner-Tavan's network
Self-organizing maps
Summary
Further reading
15
Fundamentals of Ensemble Learning
Fundamentals of Ensemble Learning
Ensemble learning fundamentals
Random forests
AdaBoost
Summary
Further reading
16
Advanced Boosting Algorithms
Advanced Boosting Algorithms
Gradient boosting
Ensembles of voting classifiers
Ensemble learning as model selection
Summary
Further reading
17
Modeling Neural Networks
Modeling Neural Networks
The basic artificial neuron
The perceptron
Multilayer Perceptrons (MLPs)
The back-propagation algorithm
Summary
Further reading
18
Optimizing Neural Networks
Optimizing Neural Networks
Optimization algorithms
Regularization and Dropout
Batch normalization
Summary
Further reading
19
Deep Convolutional Networks
Deep Convolutional Networks
Deep convolutional networks
Convolutional operators
Pooling layers
Example of a deep convolutional network with TensorFlow and Keras
Summary
Further reading
20
Recurrent Neural Networks
Recurrent Neural Networks
Recurrent networks
Long Short-Term Memory (LSTM)
Transfer learning
Summary
Further reading
21
Autoencoders
Autoencoders
Autoencoders
Denoising autoencoders
Sparse autoencoders
Variational autoencoders
Summary
Further reading
22
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial Networks
Adversarial training
Deep Convolutional GANs
Wasserstein GAN
Summary
Further reading
23
Deep Belief Networks
Deep Belief Networks
Introduction to Markov random fields
Restricted Boltzmann Machines
Deep Belief Networks
Summary
Further reading
24
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Fundamental concepts of RL
Policy iteration
Value iteration
The TD(0) algorithm
Summary
Further reading
25
Advanced Policy Estimation Algorithms
Advanced Policy Estimation Algorithms
TD(λ) algorithm
SARSA algorithm
Q-learning
Direct policy search through policy gradient
Summary
Further reading
26
Other Books You May Enjoy
Other Books You May Enjoy
27
Index
Index

Co-Training

Co-Training is another very simple but effective semi-supervised approach, proposed by Blum and Mitchell (in Blum A., Mitchell T., Combining Labeled and Unlabeled Data with Co-Training, 11th Annual Conference on Computational Learning Theory, 1998) as an alternative strategy when the dataset is a multidimensional one, and different groups of features encode different but still peculiar aspects of each class. Co-Training is effective only in scenarios where the data points can be theoretically classified using only a part of the features (even if with a light performance loss). As we're going to see, the redundancy becomes helpful in presence of an unlabeled sample, to compensate for the lack of knowledge that a single classifier might have. On the contrary, if every data point contains features that cannot be split into two separate and autonomous groups, this method is ineffective.

Co-Training theory

Let's suppose we have a labeled dataset {XL, YL} with...

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