Using **Hidden Markov Models** (**HMMs**) is a technique for modeling Markov processes with unobserved states. They are a special case of **Dynamic Bayesian Networks** (**DBNs**) but have been found to perform well in a wide range of problems. One of the areas where HMMs are used a lot is speech recognition because HMMs are able to provide a very natural way to model speech data. This book starts by introducing the theoretical aspects of HMMs from the basics of probability theory, and then talks about the different applications of HMMs.

#### Hands-On Markov Models with Python

##### By :

#### Hands-On Markov Models with Python

##### By:

#### Overview of this book

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.
Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.
In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.
By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.

Table of Contents (11 chapters)

Preface

Free Chapter

Introduction to the Markov Process

Hidden Markov Models

State Inference - Predicting the States

Parameter Learning Using Maximum Likelihood

Parameter Inference Using the Bayesian Approach

Time Series Predicting

Natural Language Processing

2D HMM for Image Processing

Markov Decision Process

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