Hands-On Markov Models with Python

"Hands-On Markov Models with Python" is a comprehensive guide to understanding and implementing Hidden Markov Models (HMMs) using Python. The book delves into theoretical and practical aspects, providing real-world scenarios and datasets to learn from. Dive into topics like state and parameter inference, time series analysis, natural language processing, and more.

What this Book will help me do

• Understand the foundations of Markov processes and Hidden Markov Models (HMMs).
• Learn how to use Python packages to implement and analyze HMMs effectively.
• Master inference techniques, including state and parameter inference, using realistic datasets.
• Apply HMM concepts to solve problems in time series, NLP, and image processing.
• Explore advanced topics such as reinforcement learning using HMMs for algorithmic trading applications.

Author(s)

The authors of this book, Ankan and Panda, are experts in the fields of data science and machine learning. With their experience in applying statistical models to practical problems, they bring hands-on expertise to this guide. Their approachable method of teaching ensures readers can effectively learn and apply complex concepts like HMMs in Python.

Who is it for?

This book is ideal for data analysts, data scientists, and machine learning practitioners looking to expand their modeling toolset. Whether you're interested in statistics, natural language processing, or algorithmic trading, this guide provides a systematic approach. A basic understanding of Python and familiarity with machine learning concepts are recommended to get the full benefit.