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

GLMs

Let's start our analysis of regression models by defining the context we're working with. A regression is a model that associates an input vector, , with one or more continuous dependent variables (for simplicity, we're going to refer to single outputs), . In a general scenario, there's no explicit dependence on time, even if regression models are often employed to model time series. The main difference is that, in the latter, the order of the data points cannot be changed, because there are often inter-dependencies. On the other hand, a generic regression can be used to model time-independent phenomena, and, in the context of GLMs, we're initially assuming that we work with stateless associations where the output value depends only on the input vector. In such cases, it's also possible to shuffle the dataset without changing the final result (of course, this is not true if the output at time t depends, for example, on y_t-1, which is a function of...