Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Language: English
Published by Packt Publishing, 2018
- Softcover
- New
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In English.
Seller Inventory # ria9781788625449_new
- Title
- Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
- Author
- Ankur Ankan; Abinash Panda
- Publisher
- Packt Publishing
- Publication year
- 2018
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1788625447
- ISBN 13
- 9781788625449
- Item weight
- 438 grams
Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn
Key Features
- Build a variety of Hidden Markov Models (HMM)
- Create and apply models to any sequence of data to analyze, predict, and extract valuable insights
- Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation
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.
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
Who this book is for
Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data.
Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book
Table of Contents
- Introduction to Markov Process
- Hidden Markov Models
- State Inference: Predicting the states
- Parameter Inference using Maximum Likelihood
- Parameter Inference using Bayesian Approach
- Time Series: Predicting Stock Prices
- Natural Language Processing: Teaching machines to talk
- 2D-HMM for Image Processing
- Reinforcement Learning: Teaching a robot to cross a maze
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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