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.
Table of Contents (28 chapters)
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Introduction to Semi-Supervised Learning

Semi-supervised learning is a machine learning branch that tries to solve problems that include both labeled and unlabeled data, employing concepts that include characteristics of both clustering and classification methods.

The high availability of unlabeled samples, and the difficulty of labeling huge datasets correctly, drove many researchers to investigate the best approaches that allow extending the knowledge provided by the labeled samples to a larger unlabeled population, without loss of accuracy. In this chapter, we're going to introduce this branch of machine learning and we'll discuss:

  • The semi-supervised scenario
  • The different approaches to semi-supervised learning
  • The assumptions needed to efficiently operate in such a scenario

We'll then move on to present several semi-supervised learning algorithms and show Python coded examples of them in practice. Example algorithms include: