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

Summary

In this chapter, we have introduced the reader to the most important concepts concerning linear models and regressions. In the first part, we discussed the features of a GLM, focusing attention on how to fit the models and how to avoid the most common problems.

We also analyzed how to include regularization penalties through ridge and lasso regressions and how it's possible to exploit the linear framework also when the dataset is non-linear through an appropriate polynomial transformation. We also compared the results with the one obtained using an isotonic regression and we analyzed the reasons for preferring either the former or the latter. Another important topic discussed in the chapter is risk modeling using logistic regression penalized with lasso to perform an automatic feature selection.

In the next chapter, we start discussing the basic concepts and of time-series analysis, focusing on the properties on the most important models (ARMA and ARIMA) that are...