Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python

Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python

Author: William Sullivan

Publisher: PublishDrive

Published: 2019-04-29

Total Pages: 73

ISBN-13:

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Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python o you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events. Extend your knowledge of machine learning, python programming & algorithms. What you'll Learn · Mathematics Behind Markov Algorithms · 3 Main Problems Of Markov Models And How To Overcome Them · Uses And Applications For Machine Learning · Python Programming · Speech Recognition · Weather Reporting · The Markov Rule And Markov's Model · Fundamental Axioms Of Statistics And Probability · Solutions · Theories · Artificial Intelligence · Bayesian Inference · Important Tools Used With HMM · And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov Models in python programming. This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price. What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!


Book Synopsis Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python by : William Sullivan

Download or read book Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python written by William Sullivan and published by PublishDrive. This book was released on 2019-04-29 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python o you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events. Extend your knowledge of machine learning, python programming & algorithms. What you'll Learn · Mathematics Behind Markov Algorithms · 3 Main Problems Of Markov Models And How To Overcome Them · Uses And Applications For Machine Learning · Python Programming · Speech Recognition · Weather Reporting · The Markov Rule And Markov's Model · Fundamental Axioms Of Statistics And Probability · Solutions · Theories · Artificial Intelligence · Bayesian Inference · Important Tools Used With HMM · And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov Models in python programming. This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price. What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!

Markov Models

Markov Models

Author: Robert Wilson

Publisher: Createspace Independent Publishing Platform

Published: 2017-06-10

Total Pages: 150

ISBN-13: 9781548002206

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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)


Book Synopsis Markov Models by : Robert Wilson

Download or read book Markov Models written by Robert Wilson and published by Createspace Independent Publishing Platform. This book was released on 2017-06-10 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Supervised and Unsupervised Machine Learning

Markov Models Supervised and Unsupervised Machine Learning

Author: William Sullivan

Publisher: Createspace Independent Publishing Platform

Published: 2017-09-03

Total Pages: 124

ISBN-13: 9781976050008

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Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook Do you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events Extend your knowledge of machine learning, python programming & algorithms What you'll Learn Mathematics Behind Markov Algorithms 3 Main Problems Of Markov Models And How To Overcome Them Uses And Applications For Machine Learning Python Programming Speech Recognition Weather Reporting The Markov Rule And Markov's Model Fundamental Axioms Of Statistics And Probability Solutions Theories Artificial Intelligence Bayesian Inference Important Tools Used With HMM And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level You will learn the ins and outs of machine learning, and its real world applications Also, specifically you will discover practical implementations of Markov Models in python programming This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!


Book Synopsis Markov Models Supervised and Unsupervised Machine Learning by : William Sullivan

Download or read book Markov Models Supervised and Unsupervised Machine Learning written by William Sullivan and published by Createspace Independent Publishing Platform. This book was released on 2017-09-03 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook Do you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events Extend your knowledge of machine learning, python programming & algorithms What you'll Learn Mathematics Behind Markov Algorithms 3 Main Problems Of Markov Models And How To Overcome Them Uses And Applications For Machine Learning Python Programming Speech Recognition Weather Reporting The Markov Rule And Markov's Model Fundamental Axioms Of Statistics And Probability Solutions Theories Artificial Intelligence Bayesian Inference Important Tools Used With HMM And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level You will learn the ins and outs of machine learning, and its real world applications Also, specifically you will discover practical implementations of Markov Models in python programming This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!

Hands-On Markov Models with Python

Hands-On Markov Models with Python

Author: Ankur Ankan

Publisher: Packt Publishing Ltd

Published: 2018-09-27

Total Pages: 172

ISBN-13: 1788629337

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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


Book Synopsis Hands-On Markov Models with Python by : Ankur Ankan

Download or read book Hands-On Markov Models with Python written by Ankur Ankan and published by Packt Publishing Ltd. This book was released on 2018-09-27 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Thoughtful Machine Learning with Python

Thoughtful Machine Learning with Python

Author: Matthew Kirk

Publisher: "O'Reilly Media, Inc."

Published: 2017-01-16

Total Pages: 220

ISBN-13: 1491924101

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Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms


Book Synopsis Thoughtful Machine Learning with Python by : Matthew Kirk

Download or read book Thoughtful Machine Learning with Python written by Matthew Kirk and published by "O'Reilly Media, Inc.". This book was released on 2017-01-16 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms

Markov Models

Markov Models

Author: Duo Code

Publisher: Createspace Independent Publishing Platform

Published: 2017-05-29

Total Pages: 80

ISBN-13: 9781546999799

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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


Book Synopsis Markov Models by : Duo Code

Download or read book Markov Models written by Duo Code and published by Createspace Independent Publishing Platform. This book was released on 2017-05-29 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Hidden Markov Models and Applications

Hidden Markov Models and Applications

Author: Nizar Bouguila

Publisher: Springer Nature

Published: 2022-05-19

Total Pages: 303

ISBN-13: 3030991423

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This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.


Book Synopsis Hidden Markov Models and Applications by : Nizar Bouguila

Download or read book Hidden Markov Models and Applications written by Nizar Bouguila and published by Springer Nature. This book was released on 2022-05-19 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.

Hidden Semi-Markov Models

Hidden Semi-Markov Models

Author: Shun-Zheng Yu

Publisher: Morgan Kaufmann

Published: 2015-10-22

Total Pages: 209

ISBN-13: 0128027711

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Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science. Discusses the latest developments and emerging topics in the field of HSMMs Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. Shows how to master the basic techniques needed for using HSMMs and how to apply them.


Book Synopsis Hidden Semi-Markov Models by : Shun-Zheng Yu

Download or read book Hidden Semi-Markov Models written by Shun-Zheng Yu and published by Morgan Kaufmann. This book was released on 2015-10-22 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science. Discusses the latest developments and emerging topics in the field of HSMMs Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. Shows how to master the basic techniques needed for using HSMMs and how to apply them.

Advanced Lectures on Machine Learning

Advanced Lectures on Machine Learning

Author: Olivier Bousquet

Publisher: Springer

Published: 2011-03-22

Total Pages: 246

ISBN-13: 3540286500

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Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.


Book Synopsis Advanced Lectures on Machine Learning by : Olivier Bousquet

Download or read book Advanced Lectures on Machine Learning written by Olivier Bousquet and published by Springer. This book was released on 2011-03-22 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Machine Learning Foundations

Machine Learning Foundations

Author: Taeho Jo

Publisher: Springer Nature

Published: 2021-02-12

Total Pages: 391

ISBN-13: 3030659003

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This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.


Book Synopsis Machine Learning Foundations by : Taeho Jo

Download or read book Machine Learning Foundations written by Taeho Jo and published by Springer Nature. This book was released on 2021-02-12 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.