Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

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

1. Introduction to Markov Process
2. Hidden Markov Models
3. State Inference: Predicting the states
4. Parameter Inference using Maximum Likelihood
5. Parameter Inference using Bayesian Approach
6. Time Series: Predicting Stock Prices
7. Natural Language Processing: Teaching machines to talk
8. 2D-HMM for Image Processing
9. Reinforcement Learning: Teaching a robot to cross a maze

發揮TensorFlow、pgmpy和hmmlearn在隱藏馬可夫模型中的非監督機器學習能力

主要特點:
- 構建各種隱藏馬可夫模型（HMM）
- 創建並應用模型於任何數據序列以進行分析、預測和提取有價值的見解
- 使用自然語言處理（NLP）技術和2D-HMM模型進行圖像分割

書籍描述:
隱藏馬可夫模型（HMM）是一種基於馬可夫鏈概念的統計模型。《Hands-On Markov Models with Python》通過解決實際問題，幫助您掌握HMM和不同推理算法。書中的實例幫助您通過使用馬可夫模型概念簡化機器學習的流程，使其對每個人都易於理解。

一旦您掌握了馬可夫鏈的基本概念，您將通過實際例子瞭解馬可夫過程、模型和類型。在掌握這些基礎知識後，您將學習不同推理算法的應用，並將其應用於狀態和參數推理。此外，您還將探索推理的貝葉斯方法，並學習如何在HMM中應用它。

在後續章節中，您將學習如何使用Python在時間序列分析和自然語言處理（NLP）中使用HMM。您還將學習如何使用2D-HMM對圖像進行分割。最後，您將瞭解如何使用Q-Learning將HMM應用於強化學習（RL），並將此技術應用於單股和多股算法交易。

通過閱讀本書，您將掌握如何在複雜數據集上構建自己的馬可夫和隱藏馬可夫模型，以應用於項目中。

您將學到:
- 探索HMM的理論和實踐的平衡
- 使用不同數據集在Python中實現HMM，並使用不同的套件
- 瞭解多種推理算法，以及如何選擇正確的算法解決問題
- 在HMM中發展貝葉斯推理方法
- 在金融、自然語言處理（NLP）和圖像處理中實現HMM
- 使用Viterbi算法確定HMM中最可能的隱藏狀態序列

本書適合對象:
《Hands-On Markov Models with Python》適合數據分析師、數據科學家或機器學習開發人員，並希望提升機器學習知識和技能的讀者。本書還將幫助您通過將其應用於任何數據序列來構建自己的隱藏馬可夫模型。

預期讀者具備機器學習和Python編程語言的基本知識，以獲得最大的收益。

目錄:
1. 馬可夫過程簡介
2. 隱藏馬可夫模型
3. 狀態推理：預測狀態
4. 使用最大似然進行參數推理
5. 使用貝葉斯方法進行參數推理
6. 時間序列：預測股價
7. 自然語言處理：教機器說話
8. 2D-HMM圖像處理
9. 強化學習：教機器人穿越迷宮