Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Authors: Ankur Ankan – Abinash Panda
ISBN-10: 1788625447
ISBN-13: 9781788625449
Released: 2018-11-09
Print Length 页数: 178 pages
Book Description
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.
Contents
1: INTRODUCTION TO THE MARKOV PROCESS
2: HIDDEN MARKOV MODELS
3: STATE INFERENCE – PREDICTING THE STATES
4: PARAMETER LEARNING USING MAXIMUM LIKELIHOOD
5: PARAMETER INFERENCE USING THE BAYESIAN APPROACH
6: TIME SERIES PREDICTING
7: NATURAL LANGUAGE PROCESSING
8: 2D HMM FOR IMAGE PROCESSING
9: MARKOV DECISION PROCESS
What You Will Learn
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
Authors
AnkurAnkan
AnkurAnkan 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
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.
