Hands-On Markov Models with Python : 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. 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." --back cover of book Cover Title Page Copyright and Credits Packt Upsell Contributors Table of Contents Preface Chapter 1: Introduction to the Markov Process Random variables Random processes Markov processes Installing Python and packages Installation on Windows Installation on Linux Markov chains or discrete-time Markov processes Parameterization of Markov chains Properties of Markov chains Reducibility Periodicity Transience and recurrence Mean recurrence time Expected number of visits Absorbing states Ergodicity Steady-state analysis and limiting distributions Continuous-time Markov chains Exponential distributions Poisson process Continuous-time Markov chain example Continuous-time Markov chain Summary Chapter 2: Hidden Markov Models Markov models State space models The HMM Parameterization of HMM Generating an observation sequence Installing Python packages Evaluation of an HMM Extensions of HMM Factorial HMMs Tree-structured HMM Summary Chapter 3: State Inference - Predicting the States State inference in HMM Dynamic programming Forward algorithm Computing the conditional distribution of the hidden state given the observations Backward algorithm Forward-backward algorithm (smoothing) The Viterbi algorithm Summary Chapter 4: Parameter Learning Using Maximum Likelihood Maximum likelihood learning MLE in a coin toss MLE for normal distributions MLE for HMMs Supervised learning Code Unsupervised learning Viterbi learning algorithm The Baum-Welch algorithm (expectation maximization) Code Summary Chapter 5: Parameter Inference Using the Bayesian Approach Bayesian learning Selecting the priors Intractability Bayesian learning in HMM Approximating required integrals Sampling methods Laplace approximations Stolke and Omohundro's method Variational methods Code Summary Chapter 6: Time Series Predicting Stock price prediction using HMM Collecting stock price data Features for stock price prediction Predicting price using HMM Summary Chapter 7: Natural Language Processing Part-of-speech tagging Code Getting data Exploring the data Finding the most frequent tag Evaluating model accuracy An HMM-based tagger Speech recognition Python packages for speech recognition Basics of SpeechRecognition Speech recognition from audio files Speech recognition using the microphone Summary Chapter 8: 2D HMM for Image Processing Recap of 1D HMM 2D HMMs Algorithm Assumptions for the 2D HMM model Parameter estimation using EM Summary Chapter 9: Markov Decision Process Reinforcement learning Reward hypothesis State of the environment and the agent Components of an agent The Markov reward process Bellman equation MDP Code example Summary Other Books You May Enjoy Index Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden 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 learnExplore a balance of both theoretical and practical aspects of HMMImplement HMMs using different datasets in Python using different packagesUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference in HMMsImplement HMMs in finance, natural language processing (NLP), and image processingDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithmWho this book is forHands-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 This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.