Machine Learning Algorithms And Applications

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Machine Learning Algorithms and Applications

Mettu Srinivas,G. Sucharitha,Anjanna Matta
Machine Learning Algorithms and Applications Book PDF

Author : Mettu Srinivas,G. Sucharitha,Anjanna Matta
Publisher : John Wiley & Sons
Page : 372 pages
File Size : 40,9 Mb
Release : 2021-08-10
Category : Computers
ISBN : 9781119769248

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Machine Learning Algorithms and Applications by Mettu Srinivas,G. Sucharitha,Anjanna Matta Pdf

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

Machine Learning

Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier
Machine Learning Book PDF

Author : Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier
Publisher : CRC Press
Page : 227 pages
File Size : 46,8 Mb
Release : 2016-08-19
Category : Computers
ISBN : 9781498705394

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Machine Learning by Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier Pdf

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Deep Learning: Algorithms and Applications

Witold Pedrycz,Shyi-Ming Chen
Deep Learning Algorithms and Applications Book PDF

Author : Witold Pedrycz,Shyi-Ming Chen
Publisher : Springer Nature
Page : 360 pages
File Size : 54,7 Mb
Release : 2019-10-23
Category : Technology & Engineering
ISBN : 9783030317607

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Deep Learning: Algorithms and Applications by Witold Pedrycz,Shyi-Ming Chen Pdf

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Machine and Deep Learning Algorithms and Applications

Uday Shankar,Andreas Spanias
Machine and Deep Learning Algorithms and Applications Book PDF

Author : Uday Shankar,Andreas Spanias
Publisher : Springer Nature
Page : 107 pages
File Size : 54,9 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031037580

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Machine and Deep Learning Algorithms and Applications by Uday Shankar,Andreas Spanias Pdf

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.

Machine Learning Algorithms for Industrial Applications

Santosh Kumar Das,Shom Prasad Das,Nilanjan Dey,Aboul-Ella Hassanien
Machine Learning Algorithms for Industrial Applications Book PDF

Author : Santosh Kumar Das,Shom Prasad Das,Nilanjan Dey,Aboul-Ella Hassanien
Publisher : Springer Nature
Page : 321 pages
File Size : 41,8 Mb
Release : 2020-07-18
Category : Technology & Engineering
ISBN : 9783030506414

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Machine Learning Algorithms for Industrial Applications by Santosh Kumar Das,Shom Prasad Das,Nilanjan Dey,Aboul-Ella Hassanien Pdf

This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics. Accordingly, there is a pressing need for novel and innovative algorithms to help us find effective solutions in industrial application areas such as media, healthcare, travel, finance, and retail. In all of these areas, data is the crucial parameter, and the main key to unlocking the value of industry. The book presents a range of intelligent algorithms that can be used to filter useful information in the above-mentioned application areas and efficiently solve particular problems. Its main objective is to raise awareness for this important field among students, researchers, and industrial practitioners.

Machine Learning Refined

Jeremy Watt,Reza Borhani,Aggelos K. Katsaggelos
Machine Learning Refined Book PDF

Author : Jeremy Watt,Reza Borhani,Aggelos K. Katsaggelos
Publisher : Cambridge University Press
Page : 597 pages
File Size : 50,8 Mb
Release : 2020-01-09
Category : Computers
ISBN : 9781108480727

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Machine Learning Refined by Jeremy Watt,Reza Borhani,Aggelos K. Katsaggelos Pdf

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David
Understanding Machine Learning Book PDF

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 45,6 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

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Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Fundamentals and Methods of Machine and Deep Learning

Pradeep Singh
Fundamentals and Methods of Machine and Deep Learning Book PDF

Author : Pradeep Singh
Publisher : John Wiley & Sons
Page : 480 pages
File Size : 50,6 Mb
Release : 2022-02-01
Category : Computers
ISBN : 9781119821885

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Fundamentals and Methods of Machine and Deep Learning by Pradeep Singh Pdf

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Machine Learning

Anonim
Machine Learning Book PDF

Author : Anonim
Publisher : BoD – Books on Demand
Page : 153 pages
File Size : 50,6 Mb
Release : 2021-12-22
Category : Computers
ISBN : 9781839694844

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Machine Learning by Anonim Pdf

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

Reinforcement Learning Algorithms: Analysis and Applications

Boris Belousov,Hany Abdulsamad,Pascal Klink,Simone Parisi,Jan Peters
Reinforcement Learning Algorithms Analysis and Applications Book PDF

Author : Boris Belousov,Hany Abdulsamad,Pascal Klink,Simone Parisi,Jan Peters
Publisher : Springer Nature
Page : 197 pages
File Size : 43,7 Mb
Release : 2021-01-02
Category : Technology & Engineering
ISBN : 9783030411886

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Reinforcement Learning Algorithms: Analysis and Applications by Boris Belousov,Hany Abdulsamad,Pascal Klink,Simone Parisi,Jan Peters Pdf

This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.

Machine Learning and Big Data

Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad
Machine Learning and Big Data Book PDF

Author : Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad
Publisher : John Wiley & Sons
Page : 544 pages
File Size : 55,8 Mb
Release : 2020-09-01
Category : Computers
ISBN : 9781119654742

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Machine Learning and Big Data by Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad Pdf

This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

Machine Learning and Its Applications

PETER. WLODARCZAK
Machine Learning and Its Applications Book PDF

Author : PETER. WLODARCZAK
Publisher : CRC Press
Page : 188 pages
File Size : 40,7 Mb
Release : 2021-06-30
Category : Electronic
ISBN : 1032086777

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Machine Learning and Its Applications by PETER. WLODARCZAK Pdf

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

Machine Learning

Andrea Mechelli,Sandra Vieira
Machine Learning Book PDF

Author : Andrea Mechelli,Sandra Vieira
Publisher : Academic Press
Page : 412 pages
File Size : 55,6 Mb
Release : 2019-11-14
Category : Medical
ISBN : 9780128157404

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Machine Learning by Andrea Mechelli,Sandra Vieira Pdf

Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Machine Learning and Its Application

Indranath Chatterjee
Machine Learning and Its Application Book PDF

Author : Indranath Chatterjee
Publisher : Unknown
Page : 356 pages
File Size : 41,7 Mb
Release : 2021-12-22
Category : Electronic
ISBN : 1681089424

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Machine Learning and Its Application by Indranath Chatterjee Pdf

Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering. Key features: - 8 organized chapters on core concepts of machine learning for learners - Accessible text for beginners unfamiliar with complex mathematical concepts - Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning and predictive statistics - Advanced topics such as deep learning and feature engineering provide additional information - Introduces readers to python programming with examples of code for understanding and practice - Includes a summary of the text and a dedicated section for references Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.

Machine Learning Algorithms

Giuseppe Bonaccorso
Machine Learning Algorithms Book PDF

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
Page : 360 pages
File Size : 51,9 Mb
Release : 2017-07-24
Category : Computers
ISBN : 9781785884511

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Machine Learning Algorithms by Giuseppe Bonaccorso Pdf

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.