This is the code repository for Hands-On Markov Models with Python, published by Packt.
Implement probabilistic models for learning complex data sequences using the Python ecosystem
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
from hmmlearn.hmm import GaussianHMM
import numpy as np
import matplotlib.pyplot as plt
Following is what you need for this book:
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
With the following software and hardware list you can run all code files present in the book (Chapter 1-9).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Python 3.5, numpy 1.15.1 | Linux, Windows or MacOS |
| 2 | Python 3.5, numpy 1.15.1, hmmlearn 0.2.0, matplotlib 2.2.3 | Linux, Windows or MacOS |
| 3 | Python 3.5, numpy 1.15.1 | Linux, Windows or MacOS |
| 4 | Python 3.5, numpy 1.15.1, hmmlearn 0.2.0 | Linux, Windows or MacOS |
| 6 | Python 3.5, numpy 1.15.1, pandas 0.23.4, hmmlearn 0.2.0, matplotlib 2.2.3, scikit-learn 0.19.2, tqdm 4.26, docopt 0.6.2, requests 2.19.1 | Linux, Windows or MacOS |
| 7 | Python 3.5, numpy 1.15.1, matplotlib 2.2.3, pomegranate 0.10.0 | Linux, Windows or MacOS |
| 9 | Python 3.5, numpy 1.15.1 | Linux, Windows or MacOS |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Machine Learning Algorithms - Second Edition [Packt] [Amazon]
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Building Machine Learning Systems with Python - Third Edition [Packt] [Amazon]
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.
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