Before moving ahead, we need to set up Python and all the packages required to run the code examples. For all the code examples in this book, we will be using Python 3.4. All the example code in the book is also available on GitHub at https://github.com/PacktPublishing/HandsOnMarkovModelswithPython. We highly recommend using Miniconda to set up your environment for running the examples. Miniconda can be downloaded from https://conda.io/miniconda.html.
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Hands-On Markov Models with Python
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Hands-On Markov Models with Python
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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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