Hands-On Markov Models with Python

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Hands-On Markov Models with Python:Implement probabilistic models for learning complex data sequences using the Python ecosystem [color=rgb(85, 85, 85) !important]1st Edition
[color=rgb(85, 85, 85) !important]

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
  

Editorial ReviewsAbout the AuthorAnkurAnkan 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 is an undergraduate from IIT (BHU), Varanasi, and is currently working as a data scientist. He has been a contributor to open source libraries such as the Shogun machine learning toolbox and pgmpy, which he started writing along with four other members. He spends most of his free time on improving pgmpy and helping new contributors.




Product details

• File Size: 25289 KB
• Print Length: 178 pages
• Publisher: Packt Publishing; 1 edition (September 27, 2018)
• Publication Date: September 27, 2018
• Sold by: Amazon Digital Services LLC
• Language: English
• ASIN: B07CSHB8NW
  

Hands on Markov Models with Python.pdf (13.51 MB, 需要: 25 个论坛币)

2018-10-15 20:25:56 上传

Hands-On Markov Models with Python
需要: 25 个论坛币  [购买]

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏2 回帖

谢谢分享，支持一下！！！