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

Generalized Linear Models and Regression

In this chapter, we're going to introduce the concept of Generalized Linear Models (GLMs) and regression, which remain essential pillars of topics such as econometrics and epidemiology. The goal is to explain the fundamental elements and expand them, showing both the advantages and limitations, while also focusing attention on practical applications that can be effectively solved using different kind of regression techniques.

In particular, we're going to discuss the following:

GLMs
Linear regression based on ordinary and weighted least squares
Other regression techniques and when to use them, including:
- Ridge regression and its implementation
- Polynomial regression with coded examples
- Isotonic regression
- Risk modeling with lasso and logistic regression

The first concept we're going to discuss is at the center of all the other algorithms analyzed in this chapter, and which is based on the description...