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)
26
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Index

Graph-Based Semi-Supervised Learning

In this chapter, we continue our discussion about semi-supervised learning, considering a family of algorithms that are based on the graph obtained from a dataset, and the existing relationships among samples. The problems that we are going to discuss belong to two main categories: the propagation of class labels to unlabeled samples, and the use of non-linear techniques based on the manifold assumption to reduce the dimensionality of the original dataset. In particular, this chapter covers the following propagation algorithms:

  • Label propagation based on the weight matrix
  • Label propagation in scikit-learn, based on transition probabilities
  • Label spreading
  • Laplacian regularization
  • Propagation based on Markov random walks

For the manifold learning section, we're discussing the following:

  • The Isomap algorithm and the multidimensional scaling approach
  • Locally linear embedding
  • Laplacian spectral embedding...