Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
bUnleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn/b h4Key Features/h4 ulliBuild a variety of Hidden Markov Models (HMM) /li liCreate and apply models to any sequence of data to analyze, predict, and extract valuable insights /li liUse n...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2018
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bUnleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn/b h4Key Features/h4 ulliBuild a variety of Hidden Markov Models (HMM) /li liCreate and apply models to any sequence of data to analyze, predict, and extract valuable insights /li liUse natural language processing (NLP) techniques and 2D-HMM model for image segmentation /li /ul h4Book Description/h4 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. h4What you will learn/h4 ulliExplore a balance of both theoretical and practical aspects of HMM /li liImplement HMMs using different datasets in Python using different packages /li liUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problems /li liDevelop a Bayesian approach to inference in HMMs /li liImplement HMMs in finance, natural language processing (NLP), and image processing /li liDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithm /li /ul h4Who this book is for/h4 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 |
| Beschreibung: | 1 Online-Ressource (178 Seiten) |
| ISBN: | 9781788629331 |
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| 520 | |a bUnleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn/b h4Key Features/h4 ulliBuild a variety of Hidden Markov Models (HMM) /li liCreate and apply models to any sequence of data to analyze, predict, and extract valuable insights /li liUse natural language processing (NLP) techniques and 2D-HMM model for image segmentation /li /ul h4Book Description/h4 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. | ||
| 520 | |a 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. | ||
| 520 | |a h4What you will learn/h4 ulliExplore a balance of both theoretical and practical aspects of HMM /li liImplement HMMs using different datasets in Python using different packages /li liUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problems /li liDevelop a Bayesian approach to inference in HMMs /li liImplement HMMs in finance, natural language processing (NLP), and image processing /li liDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithm /li /ul h4Who this book is for/h4 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 | ||
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| 912 | |a ZDB-5-WPSE | ||
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| illustrated | Not Illustrated |
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| isbn | 9781788629331 |
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| spelling | Ankan, Ankur Verfasser aut Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankan, Ankur 1 Birmingham Packt Publishing Limited 2018 1 Online-Ressource (178 Seiten) txt rdacontent c rdamedia cr rdacarrier bUnleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn/b h4Key Features/h4 ulliBuild a variety of Hidden Markov Models (HMM) /li liCreate and apply models to any sequence of data to analyze, predict, and extract valuable insights /li liUse natural language processing (NLP) techniques and 2D-HMM model for image segmentation /li /ul h4Book Description/h4 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. h4What you will learn/h4 ulliExplore a balance of both theoretical and practical aspects of HMM /li liImplement HMMs using different datasets in Python using different packages /li liUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problems /li liDevelop a Bayesian approach to inference in HMMs /li liImplement HMMs in finance, natural language processing (NLP), and image processing /li liDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithm /li /ul h4Who this book is for/h4 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 COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks Panda, Abinash Sonstige oth |
| spellingShingle | Ankan, Ankur Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| title | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem |
| title_auth | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem |
| title_exact_search | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem |
| title_exact_search_txtP | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem |
| title_full | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankan, Ankur |
| title_fullStr | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankan, Ankur |
| title_full_unstemmed | Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankan, Ankur |
| title_short | Hands-On Markov Models with Python |
| title_sort | hands on markov models with python implement probabilistic models for learning complex data sequences using the python ecosystem |
| title_sub | Implement probabilistic models for learning complex data sequences using the Python ecosystem |
| topic | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| topic_facet | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| work_keys_str_mv | AT ankanankur handsonmarkovmodelswithpythonimplementprobabilisticmodelsforlearningcomplexdatasequencesusingthepythonecosystem AT pandaabinash handsonmarkovmodelswithpythonimplementprobabilisticmodelsforlearningcomplexdatasequencesusingthepythonecosystem |