Hands On Markov Models With Python
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Hands On Markov Models With Python
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Author : Ankur Ankan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-27
Hands On Markov Models With Python written by Ankur Ankan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-27 with Computers categories.
Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key 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 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 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 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
Markov Models
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Author : Robert Wilson
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-06-10
Markov Models written by Robert Wilson and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-10 with Machine learning categories.
Do you want to become a data science Savvy? If reading about Markov models, stochastic processes, and probabilities leaves you scratching your head, then you have definitely come to the right place. If you are looking for the most no-nonsense guide that will keep you on the right course during the turbulent ride filled with scientific enigmas, machine learning, and predicting probabilities of hidden, unobservable states, then you have found your perfect companion. This book will Cover: What is Markov models How to make predictions with Markov Models How to learn without supervision How do Markov Models use prediction? Hidden Markov Models and how to use them The secrets of Markov Chains Tips and tricks on how to use Markov Models and machine learning Markov Models with Python Markov Models Examples and predictions How to build and implement HMM algorithms How to use Markov Models to master machine learning The secrets of Supervised and unsupervised machine learning The three components of Hidden Markov Models And much, much more! By the end of this book, I guarantee that you will dive easily into the data science world. Save yourself the hard work and frustration by downloading this book today. Download your free copy today (Kindle Unlimited only)
Markov Models And Unsupervised Machine Learning
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Author : Peter Wanjala
language : en
Publisher:
Release Date : 2018-05-28
Markov Models And Unsupervised Machine Learning written by Peter Wanjala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-28 with categories.
The primary aim of machine learning is to train the computers or machine to learn on their own and make informed decisions in a relatively shorter time than what human beings can do. While the promising applications of machine learning continue to expand beyond the realms of human imagination, the time and effort required to train such optimized systems are becoming a hindrance. All these dynamics point to the treasured potentials of unsupervised machine learning algorithms, which if done correctly can allow systems to learn and infer largely without human intervention. As the field grows further, the potentials of unsupervised machine learning will be revolutionary in both social and economic arena. Markov models--and specifically the Hidden Markov Models--continues to inspire confidence among machine learning enthusiasts. Hidden Markov Models are today providing support for complex problems that involve uncertainties in a continuous period. With the ever-increasing computing power, these models are likely to play an active role in the growth of machine learning applications.That is why you should keep abreast of the latest developments in Markov models and unsupervised learning in Python programming. The book explores all the concepts about Markov models and unsupervised learning that matter in AI. Chapter one introduces you to unsupervised machine learning where you will learn the main ideas on how self-learning can take place on a computer.Chapter two explores Markov models in great detail. In chapter three, you will learn how Markov models can be applied in machine learning. Chapter four dwells on implementing Markov models in Python programming language. Finally, Chapter Five explores some of the applications of Hidden Markov Models in real-life situations. It is my hope that this book will enrich your knowledge about Markov models and unsupervised machine learning. Good luck.
Markov Models
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Author : Duo Code
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-05-29
Markov Models written by Duo Code and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-29 with categories.
Do you want to MASTER data science? Learn how MACHINE LEARNING systems can carry out multifaceted processes by learning from data? Understand MARKOV MODELS and how they can help your correctly forecast future events? Want to explore practical implementations of Markov models in PYTHON PROGRAMMING environment? Then you should DOWNLOAD your copy today The aim of machine learning is to train the computers or machine to learn on its own and make informed decisions in a relatively shorter time than what human beings can do. The primary objective of this book is to provide you with all the ins and outs of Markov models and unsupervised machine learning over a range of multi-faceted applications. Specifically, the book will explore practical implementations of Markov models in Python programming environment. You'll discover: - Types of machine learning algorithms - The mathematics behind markov algorithms - Application of markov models in python programming - Application of markov models in - gaming - Speech recognition - Weather reporting and much much more! DOWNLOAD YOUR COPY TODAY TO GAIN A HUGE ADVANTAGE OVER YOUR COMPETITORS
Hands On Simulation Modeling With Python
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-17
Hands On Simulation Modeling With Python written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-17 with Computers categories.
Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features Learn to create a digital prototype of a real model using hands-on examples Evaluate the performance and output of your prototype using simulation modeling techniques Understand various statistical and physical simulations to improve systems using Python Book Description Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learn Gain an overview of the different types of simulation models Get to grips with the concepts of randomness and data generation process Understand how to work with discrete and continuous distributions Work with Monte Carlo simulations to calculate a definite integral Find out how to simulate random walks using Markov chains Obtain robust estimates of confidence intervals and standard errors of population parameters Discover how to use optimization methods in real-life applications Run efficient simulations to analyze real-world systems Who this book is for Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
Markov Models
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Author : Steven Taylor
language : en
Publisher: Steven Taylor
Release Date : 2020-07-14
Markov Models written by Steven Taylor and has been published by Steven Taylor this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-14 with Computers categories.
Markov Models This book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling. Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital as well as when events occur more than once. This book highlights several applications of Markov Models. Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits. By Downloading This Book Now You Will Discover: Hidden Markov Models Dynamic Bayesian Networks Stepwise Mutations using the Wright Fisher Model Using Normalized Algorithms to Update the Formulas Types of Markov Processes Important Tools used with HMM Machine Learning And much much more! Download this book now and learn more about Markov Models!
Probability
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Author : Steven Taylor
language : en
Publisher: Steven Taylor
Release Date : 2020-09-09
Probability written by Steven Taylor and has been published by Steven Taylor this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-09 with Mathematics categories.
A Book Bundle of Probability with Permutations and Markov Models Get two books in one now!! Probability with Permutations: An Introduction to Probability and Combinations Understanding probability as unique and stimulating theory which goes beyond conventional mathematics, will give you better perspective of the world around you. The first part of the book explains the fundamentals of probability in clear and easy to understand way even if you are not familiar with mathematics at all and you are just starting your journey towards this particular field of science. In the following sections of the book, the subject is explained in wider context along with importance of permutations and combinations in probability and their applications to a variety of scientific problems as well as the importance of probability in real life situations. Markov Models: An Introduction to Markov Models This book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling. Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital as well as when events occur more than once. This book highlights several applications of Markov Models. Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits. By Downloading This Book Bundle Now You Will Discover: History of Probability Explanation of Combinations Probability Using Permutations and Combinations Urn Problems Probability and Lottery Probability and Gambling Applications of Probability Hidden Markov Models Dynamic Bayesian Networks Stepwise Mutations using the Wright Fisher Model Using Normalized Algorithms to Update the Formulas Types of Markov Processes Important Tools used with HMM Machine Learning And much much more! Download this book bundle now and learn more about Probability with Permutations and Markov Models!
Hands On Q Learning With Python
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Author : Nazia Habib
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-19
Hands On Q Learning With Python written by Nazia Habib and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-19 with Mathematics categories.
Leverage the power of reward-based training for your deep learning models with Python Key FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
Hands On Unsupervised Learning Using Python
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Author : Ankur A. Patel
language : en
Publisher: O'Reilly Media
Release Date : 2019-02-21
Hands On Unsupervised Learning Using Python written by Ankur A. Patel and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
Hands On Data Science And Python Machine Learning
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Author : Frank Kane
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-31
Hands On Data Science And Python Machine Learning written by Frank Kane and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-31 with Computers categories.
This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.