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

View More author details

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

Hidden Markov Models

Hidden Markov Models are probabilistic algorithms that can be employed in all those contexts where it's impossible to measure the state of a system (we can only model it as a stochastic variable with a known transition probability), but it's possible to access some data connected to it. An example can be a complex engine that is made up of a large number of parts. We can define some internal states and learn a transition probability matrix (we're going to learn how to do that), but we can only receive measures provided by specific sensors.

Let's consider a stochastic process X(t) that can assume N different states: s₁, s₂, …, s_N with first-order Markov chain dynamics. Let's also suppose that we cannot observe the state of X(t), but we have access to another process O(t), connected to X(t), which produces observable outputs (often known as emissions). The resulting process is called a Hidden Markov Model (HMM), and a generic...