Ankur Ankan - Hands-On Markov Models with Python

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Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions.

Abinash Panda has been a data scientist for more than 4 years. He has worked at multiple early-stage start-ups and helped them build their data analytics pipelines. He loves to munge, plot, and analyze data. He has been a speaker at Python conferences. These days, he is busy co-founding a start-up. He has contributed to books on probabilistic graphical models by Packt Publishing.

Abdullah al Mamun is a professional software engineer and researcher. He has completed his graduation from rajshahi university of engineering & technology (RUET) and BSc in Computer Science and Engineering (CSE). Currently, he is working as a Senior Executive Officer of the Software section in Primeasia University. As a professional software engineer, he has experience in object-oriented design, software architectures, design patterns, test-driven development, and project management. Also, he is interested in research in the fields of artificial intelligence, neural network, pattern recognition, and machine learning. His research has been published in different international journals and conferences, including IEEE.

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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.

A basic understanding of probability theory, linear algebra, and calculus will make reading this book a lot easier. For the code examples, basic familiarity with Python programming is expected.

, Introduction to Markov Process , starts with a discussion of basic probability theory, and then introduces Markov chains. The chapter also talks about the different types of Markov chain classifying based on continuous or discrete states and time intervals.

, Hidden Markov Models , builds on the concept of Markov processes and DBNs to introduce the concepts of the HMM.

, State Inference Predicting the States , introduces algorithms that can be used to predict the states of a defined HMM. The chapter introduces the Forward algorithm, the backward algorithm, the forward-backward algorithm, and the Viterbi algorithm.

, Parameter Inference Using Maximum Likelihood , discusses the basics of maximum likelihood learning. The chapter then moves on to applying maximum likelihood learning in the case of HMMs and introduces the Viterbi learning algorithm and Baum-Welch algorithm.