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

October 31, 2020
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 inHidden Markov Models using TensorFlow, pgmpy, andhmmlearnKey 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-HMMmodel for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on theMarkov chain concept. Hands-On Markov Models with Python helps youget to grips with HMMs and different inference algorithms byworking on real-world problems. The hands-on examples explored inthe book help you simplify the process flow in machine learning byusing Markov model concepts, thereby making it accessible toeveryone.Once you’ve covered the basic concepts of Markov chains, you’llget insights into Markov processes, models, and types with the helpof practical examples. After grasping these fundamentals, you’llmove on to learning about the different algorithms used ininferences and applying them in state and parameter inference. Inaddition to this, you’ll explore the Bayesian approach of inferenceand learn how to apply it in HMMs.In further chapters, you’ll discover how to use HMMs in timeseries analysis and natural language processing (NLP) using Python.You’ll also learn to apply HMM to image processing using 2D-HMM tosegment images. Finally, you’ll understand how to apply HMM forreinforcement learning (RL) with the help of Q-Learning, and usethis technique for single-stock and multi-stock algorithmictrading.By the end of this book, you will have grasped how to build yourown Markov and hidden Markov models on complex datasets in order toapply them to projects.What you will learnExplore a balance of both theoretical and practical aspects ofHMMImplement HMMs using different datasets in Python usingdifferent packagesUnderstand multiple inference algorithmsand how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference inHMMsImplement HMMs in finance, natural language processing(NLP), and image processing*Determine the most likely sequence of hidden states in an HMMusing the Viterbi algorithmWho this book is forHands-On Markov Models with Python is for you if you are a dataanalyst, data scientist, or machine learning developer and want toenhance your machine learning knowledge and skills. This book willalso help you build your own hidden Markov models by applying themto any sequence of data.Basic knowledge of machine learning and the Python programminglanguage is expected to get the most out of the bookTable of ContentsIntroduction to Markov ProcessHidden Markov ModelsState Inference: Predicting the statesParameterInference using Maximum LikelihoodParameter Inference using Bayesian ApproachTimeSeries: Predicting Stock PricesNatural Language Processing: Teaching machines totalk2D-HMM for Image Processing*Reinforcement Learning: Teaching a robot to cross a maze