You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

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...