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

Summary

In this chapter, we presented the EM algorithm, explaining the reasons that justify its application in many statistical learning contexts. We also discussed the fundamental role of hidden (latent) variables in order to derive an expression that is easier to maximize (the Q function).

We applied the EM algorithm to solve a simple parameter estimation problem and afterward to prove the Gaussian Mixture estimation formulas. We showed how it's possible to employ the scikit-learn implementation instead of writing the whole procedure from scratch (like in Chapter 3, Introduction to Semi-Supervised Learning).

In the next chapter, we are going to introduce and analyze three different approaches to component extraction: Factor Analysis, PCA, and FastICA.