Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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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, …, sN 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...