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
Other Books You May Enjoy
27
Index

Component Analysis and Dimensionality Reduction

In this chapter, we're going to introduce the most common and important techniques to perform component analysis and dimensionality reduction. When working with large datasets, it's often necessary to optimize the performance of the algorithms, and one of the most reasonable ways of achieving this goal is to remove those features whose information content is negligible. The models discussed in this chapter allow us to perform a complete analysis of the components of a dataset and to select only those components that make a valuable contribution to the results. In particular, we're going to discuss the following topics:

  • Factor analysis
  • Principal Component Analysis (PCA), Kernel PCA, and Sparse PCA
  • Independent Component Analysis (ICA)
  • A brief explanation of the Hidden Markov Models (HMMs) Forward-Backward algorithm considering the EM steps

We can now start our exploration of these models...