Fundamentals Of Machine Learning Algorithms And Its Models

Author : Dr.R.Gowri, Mrs.R.A.Latha Devi, Dr.T.Dheepak, Dr.P.Kavitha, Dr.T.Suresh
Publisher : SK Research Group of Companies
Page : 202 pages
File Size : 48,9 Mb
Release : 2024-03-29
Category : Computers
ISBN : 9788119980833

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Fundamentals of Machine Learning: Algorithms and its Models by Dr.R.Gowri, Mrs.R.A.Latha Devi, Dr.T.Dheepak, Dr.P.Kavitha, Dr.T.Suresh Pdf

Dr.R.Gowri, Associate Professor, Department of Mathematics, Government College for Women (Autonomous), Kumbakonam, Tamil Nadu, India. Mrs.R.A.Latha Devi, Assistant Professor, Department of Mathematics, Sri Meenakshi Government Arts College for Women, Madurai, Tamil Nadu, India Dr.T.Dheepak, Assistant Professor, Department of Computer Science, Centre for Distance and Online Education, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India. Dr.P.Kavitha, Assistant Professor, Department of Computer Applications, Dhanalakshmi Srinivasan College of Arts and Science for Women Autonomous, Perambalur, Tamil Nadu, India. Dr.T.Suresh, Assistant Professor, Department of Artificial Intelligence & Machine Learning, K.Ramakrishnan College of Engineering, Tiruchirappalli, Tamil Nadu, India.

Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Page : 853 pages
File Size : 44,6 Mb
Release : 2020-10-20
Category : Computers
ISBN : 9780262361101

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Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy Pdf

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 44,9 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.

Author : Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar
Publisher : MIT Press
Page : 505 pages
File Size : 52,5 Mb
Release : 2018-12-25
Category : Computers
ISBN : 9780262351362

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Foundations of Machine Learning, second edition by Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar Pdf

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Author : Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad
Publisher : John Wiley & Sons
Page : 544 pages
File Size : 40,7 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.

Author : Nikhil Buduma,Nicholas Locascio
Publisher : "O'Reilly Media, Inc."
Page : 365 pages
File Size : 43,5 Mb
Release : 2017-05-25
Category : Computers
ISBN : 9781491925560

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Fundamentals of Deep Learning by Nikhil Buduma,Nicholas Locascio Pdf

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning

Author : Matthieu Cord,Pádraig Cunningham
Publisher : Springer Science & Business Media
Page : 297 pages
File Size : 45,6 Mb
Release : 2008-02-07
Category : Computers
ISBN : 9783540751717

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Machine Learning Techniques for Multimedia by Matthieu Cord,Pádraig Cunningham Pdf

Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.

Author : Hyatt Saleh
Publisher : Packt Publishing Ltd
Page : 240 pages
File Size : 52,5 Mb
Release : 2018-11-29
Category : Computers
ISBN : 9781789801767

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Machine Learning Fundamentals by Hyatt Saleh Pdf

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level Key FeaturesExplore scikit-learn uniform API and its application into any type of modelUnderstand the difference between supervised and unsupervised modelsLearn the usage of machine learning through real-world examplesBook Description As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms. What you will learnUnderstand the importance of data representationGain insights into the differences between supervised and unsupervised modelsExplore data using the Matplotlib libraryStudy popular algorithms, such as k-means, Mean-Shift, and DBSCANMeasure model performance through different metricsImplement a confusion matrix using scikit-learnStudy popular algorithms, such as Naïve-Bayes, Decision Tree, and SVMPerform error analysis to improve the performance of the modelLearn to build a comprehensive machine learning programWho this book is for Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.

Author : Kaizhu Huang,Amir Hussain,Qiu-Feng Wang,Rui Zhang
Publisher : Springer
Page : 163 pages
File Size : 47,7 Mb
Release : 2019-02-15
Category : Medical
ISBN : 9783030060732

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Deep Learning: Fundamentals, Theory and Applications by Kaizhu Huang,Amir Hussain,Qiu-Feng Wang,Rui Zhang Pdf

The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
Page : 799 pages
File Size : 51,5 Mb
Release : 2020-01-31
Category : Computers
ISBN : 9781838821913

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Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Pdf

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Author : Pradeep Singh
Publisher : John Wiley & Sons
Page : 484 pages
File Size : 52,9 Mb
Release : 2022-03-02
Category : Computers
ISBN : 9781119821250

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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.

Author : Hyatt Saleh
Publisher : Unknown
Page : 128 pages
File Size : 46,9 Mb
Release : 2019
Category : Electronic
ISBN : 1789958385

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Machine Learning Fundamentals by Hyatt Saleh Pdf

"You'll begin by learning how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithm over 1990 US Census dataset, to discover patterns and profiles, and explore the process to solve a supervised machine learning problem. Then, the focus of the course shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve performance of the algorithm by tuning hyperparameters. When it finishes, this course would have given you the skills and confidence to start programming machine learning algorithms."--Resource description page.

Author : John Anderson,E. R. Bareiss,Ryszard Stanisław Michalski
Publisher : Unknown
Page : 128 pages
File Size : 45,6 Mb
Release : 19??
Category : Electronic
ISBN : OCLC:632850500

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Proceedings of the international conference on Machine Learning by John Anderson,E. R. Bareiss,Ryszard Stanisław Michalski Pdf

Author : Jon Adams
Publisher : Green Mountain Computing
Page : 117 pages
File Size : 52,6 Mb
Release : 2024-06-30
Category : Computers
ISBN : 8210379456XXX

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AI Foundations of Machine Learning by Jon Adams Pdf

AI Foundations of Machine Learning Embark on a clarifying expedition through the vibrant world of AI with "AI Foundations of Machine Learning." This comprehensive guide is meticulously crafted for those eager to unravel the complex mechanisms driving artificial intelligence and for pioneers looking to grasp the foundational stones of future technological advancements. From the fundamentals to the futuristic prospects, this book serves as both an educational journey and an initiation into the realm where data, computation, and potential converge. Contents: Understanding Supervised Learning: Begin your journey with an exploration of supervised learning, where machines learn from data with known outcomes, setting the stage for further complexities. The Mechanics of Unsupervised Learning: Delve into the artistry of AI as it uncovers hidden patterns without explicit instructions, highlighting the autonomy of machine learning. Diving into Neural Networks: Uncover the intricacies of neural networks, AI's approximation of the human brain, capable of recognizing speech, images, and nuances in vast datasets. The Decision Tree Paradigm: Discover the decision-making processes of AI through the decision tree paradigm, where data is systematically divided and conquered. Ensemble Methods Combining Strengths: Learn about the power of ensemble methods, which combine multiple models to enhance predictive accuracy and overcome individual weaknesses. Evaluating Model Performance: Understand the critical aspect of evaluating AI model performance, ensuring the integrity and applicability of machine learning applications. Machine Learning in the Real World: Witness the transformative impact of machine learning across various industries, from healthcare to finance, and how it reshapes our interaction with technology. The Future of Machine Learning: Gaze into the future, anticipating the breakthroughs and challenges of machine learning as it becomes an omnipresent force in our lives. This book is your gateway to understanding and participating in the future of AI, equipped with the knowledge to navigate and contribute to the advancements that lie ahead. Whether you are a student, professional, or enthusiast, "AI Foundations of Machine Learning" offers valuable insights into the ever-evolving field of machine learning, encouraging readers to not only understand but also to innovate in the unfolding story of AI.

Author : Mia Williams
Publisher : Willford Press
Page : 208 pages
File Size : 49,5 Mb
Release : 2021-11-16
Category : Computers
ISBN : 1647280281

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Fundamentals of Machine Learning by Mia Williams Pdf

The scientific study of statistical models and algorithms that computer systems use in order to perform a specific task without any explicit instructions is referred to as machine learning. It relies on patterns and inference. Machine learning is a subset of artificial intelligence. The study of mathematical optimization contributes significantly to the methods, applications and theory of machine learning. Some of the different models, which are used within this field are artificial neural networks, decision trees and Bayesian networks. Machine learning is applied in various other fields such as in machine perception, agriculture, adaptive websites, bioinformatics, optimization, sentiment analysis, etc. The topics included in this book on machine learning are of utmost significance and bound to provide incredible insights to readers. It unfolds the innovative aspects of this field, which will be crucial for the progress of this field in the future. Those in search of information to further their knowledge will be greatly assisted by this book.