This book shows how to get to grips with Hidden Markov Models and different inference algorithms by working on real-world problems. It covers Markov processes, models, and types, the different algorithms used in inferences and how to apply them in state and parameter inference. Use of HMMs in time series analysis and natural language processing (NLP) using Python is also covered, as well as the use of HMM for reinforcement learning with the help of Q-Learning. Ankur Ankan and Abinash Panda use hands-on examples to simplify the process flow in machine learning by using Markov model concepts.

<ASIN:1788625447>

Author: Ankur Ankan and Abinash Panda
Publisher: Packt
Date: September 2018
Pages: 178
ISBN: 978-1788625449
Print: 1788625447
Kindle: B07CSHB8NW
Audience: Python developers interested in Markov models.
Level: Intermediate
Category: Artificial Intelligence and Python

• Explore a balance of both theoretical and practical aspects of HMM
• Implement HMMs using different datasets in Python using different packages
• Understand multiple inference algorithms and how to select the right algorithm to resolve your problems
• Develop a Bayesian approach to inference in HMMs
• Implement HMMs in finance, natural language processing (NLP), and image processing
• Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

For more Book Watch just click.

Book Watch is I Programmer's listing of new books and is compiled using publishers' publicity material. It is not to be read as a review where we provide an independent assessment. Some, but by no means all, of the books in Book Watch are eventually reviewed.

To have new titles included in Book Watch contact  BookWatch@i-programmer.info

Follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for each day's new addition to Book Watch and for new reviews.