Supervised Learning And Its Applications
DOWNLOAD
Download Supervised Learning And Its Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Supervised Learning And Its Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Supervised Learning And Its Applications
DOWNLOAD
Author : Rakesh Kumar
language : en
Publisher: Independently Published
Release Date : 2024-12
Supervised Learning And Its Applications written by Rakesh Kumar and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12 with Computers categories.
In an age where data is considered the new oil, the ability to harness and utilize this data effectively has become a critical driver of progress across industries. At the heart of this data revolution lies the concept of machine learning, a field of artificial intelligence that empowers systems to learn and make decisions from data without explicit programming. Among the various paradigms of machine learning, supervised learning stands out as one of the most powerful and widely used approaches. Supervised learning is a method where machines learn from labeled datasets to predict outcomes or classify data into predefined categories. It has revolutionized industries by enabling predictive analytics, enhancing decision-making processes, and creating intelligent systems capable of performing tasks previously thought to be exclusively human. From predicting customer behavior to diagnosing diseases and powering self-driving cars, supervised learning has become a cornerstone of modern technological advancements. This book, Supervised Learning and Its Applications, is designed to provide a comprehensive understanding of supervised learning, from its mathematical foundations to practical applications in diverse domains. Whether you are a data science enthusiast, a student embarking on your machine learning journey, or a professional seeking to deepen your knowledge, this book serves as both a guide and a resource to help you master supervised learning. What This Book Covers The journey through this book begins with an overview of machine learning and the distinction between supervised and unsupervised learning paradigms. We then dive into the key concepts and mathematics underpinning supervised learning, including linear algebra, probability, and optimization techniques, which form the foundation of its algorithms. Subsequent chapters explore the most popular supervised learning algorithms, such as linear regression, logistic regression, decision trees, support vector machines, and neural networks. For each algorithm, we explain its mechanics, strengths, limitations, and real-world applications, accompanied by practical examples and Python code implementations. Recognizing that data preparation is critical to the success of machine learning models, we dedicate chapters to feature engineering, handling imbalanced datasets, and ensuring data quality. We also discuss how to evaluate and fine-tune models using metrics, cross-validation techniques, and hyperparameter optimization. The latter part of the book delves into the applications of supervised learning in various industries, from business and healthcare to technology and environmental studies. Case studies illustrate how supervised learning is applied in real-world scenarios, providing readers with insights into its transformative potential. We also explore advanced topics, such as deep learning for supervised tasks, transfer learning, and the ethical considerations that come with building machine learning models, including bias, privacy concerns, and explainability. Finally, we look to the future, discussing emerging trends and the role of cutting-edge technologies like quantum computing in supervised learning. Why This Book Matters Supervised learning is no longer just a tool for data scientists-it has become a critical skill in an era where data-driven decision-making is paramount. Understanding how to apply supervised learning effectively can open doors to solving complex problems, optimizing processes, and creating value in innovative ways. As you progress through this book, you will gain not only a theoretical understanding of supervised learning but also practical skills to implement it in real-world scenarios. By bridging the gap between theory and practice, this book equips you to tackle challenges, drive innovation, and contribute to the evolving landscape of AI ML.
Machine Learning And Its Application A Quick Guide For Beginners
DOWNLOAD
Author : Indranath Chatterjee
language : en
Publisher: Bentham Science Publishers
Release Date : 2021-12-22
Machine Learning And Its Application A Quick Guide For Beginners written by Indranath Chatterjee and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-22 with Computers categories.
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 And Its Applications
DOWNLOAD
Author : Peter Wlodarczak
language : en
Publisher: CRC Press
Release Date : 2019-10-30
Machine Learning And Its Applications written by Peter Wlodarczak and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-30 with Computers categories.
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 A Guideline For Its Usability In Production Systems
DOWNLOAD
Author : Alexander Volz
language : en
Publisher: GRIN Verlag
Release Date : 2019-06-28
Machine Learning A Guideline For Its Usability In Production Systems written by Alexander Volz and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-28 with Technology & Engineering categories.
Bachelor Thesis from the year 2017 in the subject Engineering - Mechanical Engineering, grade: 1,3, RWTH Aachen University, language: English, abstract: This thesis provides an especially designed overview for the needs of decision makers in the production industry on the field of machine learning. By concerning economic and technological factors, as well as the individual challenges for companies, the goal of this thesis is to serve as a guideline for the usage of machine learning in production systems. After the revolutionary change caused by the introduction of the steam engine, the production line, electronics and IT, into the manufacturing industry, a new disrupting change is expected. Nowadays the rapidly increasing digitalization of the economy leads to the fourth industrial revolution. This global phenomenon is called ‘Industrie 4.0’ (GER) or ‘Smart Factory’ (US), and it combines production technology with information and communication technology. Especially, data based optimization in production is one of the predominant goals of Industrie 4.0. For the automatized analysis of large amounts of data, machine learning is an effective instrument and therefore a central element in Industrie 4.0. Recent progress in machine learning has been driven by the development of new learning algorithms and by the increasing availability of data and low-cost computation power. For many applications - from computer vision to adaptive robots – it was very difficult to devise deterministic rules. However, for these applications, it is possible to collect data, and now the idea is to use algorithms that learn from data, instead of being manually programmed. Thus, machine learning has the potential to transform data into valuable knowledge for decision making, while making improvements possible to the production system, with approaches such as predictive maintenance. The transfer of machine learning from the lab to the ‘real world’ leads to an increased interest in learning techniques, demanding further effort in explaining, on how machine learning works, and what it can be used for in other disciplines. However, the entry barrier to the diverse field of machine learning is high. With many different algorithms, theories and methods, it is hard to oversee, and therefore its influence remains limited. In addition, a recent study states that about 47% of jobs in the US are at high risk of computerization within the next decades. Therefore, employees feel insecure, and demand answers on what effect machine learning will have on their future role in the factory.
Semi Supervised Learning
DOWNLOAD
Author : Guoqiang Zhong
language : en
Publisher:
Release Date : 2018-06
Semi Supervised Learning written by Guoqiang Zhong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06 with COMPUTERS categories.
Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very scarce labeled data. The book focuses on some state-of-the-art research on semi-supervised learning. In the first chapter, Weng, Dornaika and Jin introduce a graph construction algorithm named the constrained data self-representative graph construction (CSRGC). In the second chapter, to reduce the graph construction complexity, Zhang et al. use anchors that were a special subset chosen from the original data to construct the full graph, while randomness was injected into graphs to improve the classification accuracy and deal with the high dimensionality issue. In the third chapter, Dornaika et al. introduces a kernel version of the Flexible Manifold Embedding (KFME) algorithm. In the fourth chapter, Zhang et al. present an efficient and robust graph-based transductive classification method known as the minimum tree cut (MTC), for large scale applications. In the fifth chapter, Salazar, Safont and Vergara investigated the performance of semi-supervised learning methods in two-class classification problems with a scarce population of one of the classes. In the sixth chapter, by breaking the sample identically and independently distributed (i.i.d.) assumption, one novel framework called the field support vector machine (F-SVM) with both classification (F-SVC) and regression (F-SVR) purposes is introduced. In the seventh chapter, Gong employs the curriculum learning methodology by investigating the difficulty of classifying every unlabeled example. As a result, an optimized classification sequence was generated during the iterative propagations, and the unlabeled examples are logically classified from simple to difficult. In the eighth chapter, Tang combines semi-supervised learning with geo-tagged photo streams and concept detection to explore situation recognition. This book is suitable for university students (undergraduate or graduate) in computer science, statistics, electrical engineering, or anyone else who would potentially use machine learning algorithms; professors, who research artificial intelligence, pattern recognition, machine learning, data mining and related fields; and engineers, who apply machine learning models into their products.
Machine Learning And Its Applications
DOWNLOAD
Author : Georgios Paliouras
language : en
Publisher: Springer
Release Date : 2003-06-29
Machine Learning And Its Applications written by Georgios Paliouras and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Introduction To Semi Supervised Learning
DOWNLOAD
Author : Xiaojin Geffner
language : en
Publisher: Springer
Release Date : 2009-06-08
Introduction To Semi Supervised Learning written by Xiaojin Geffner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-08 with Computers categories.
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook
Applied Machine Learning
DOWNLOAD
Author : M. Gopal
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-06-07
Applied Machine Learning written by M. Gopal and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-07 with Technology & Engineering categories.
Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: Supervised learning Statistical learning Learning with support vector machines (SVM) Learning with neural networks (NN) Fuzzy inference systems Data clustering Data transformations Decision tree learning Business intelligence Data mining And much more
Fundamentals Of Machine Learning And Its Applications
DOWNLOAD
Author : Ms. Priyanka Sharma
language : en
Publisher: Academic Guru Publishing House
Release Date : 2023-09-04
Fundamentals Of Machine Learning And Its Applications written by Ms. Priyanka Sharma and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-04 with Study Aids categories.
Fundamentals of Machine Learning and Its Applications serve as an indispensable guide for both novices and seasoned professionals delving into the intricate realm of machine learning. Authored with precision and clarity, this book navigates the multifaceted landscape of machine learning, unravelling its core concepts, methodologies, and practical implementations. The book adeptly commences by elucidating the foundational principles that underpin machine learning, progressively leading the reader through a comprehensive journey of understanding. It demystifies intricate algorithms, presenting them in a digestible manner, while also shedding light on the mathematical and statistical underpinnings that govern their functioning. One of the distinguishing features of this literary work lies in its emphasis on real-world applications. Through illuminating case studies and examples spanning diverse domains, including image recognition, natural language processing, and recommendation systems, the book bridges the gap between theory and application. This allows readers to not only grasp theoretical nuances but also to harness this knowledge in pragmatic scenarios. In a rapidly evolving field, staying abreast of the latest trends and advancements is crucial. The book acknowledges this by incorporating a section dedicated to contemporary developments, such as deep learning and neural networks. By doing so, it equips learners with insights that reflect the current state of the discipline. Fundamentals of Machine Learning and Its Applications stand as an indispensable resource, fostering a holistic comprehension of machine learning's bedrock principles and its diverse real-world implementations. It caters to eager learners aiming to fortify their expertise in this transformative domain.
Computational Finance And Its Applications
DOWNLOAD
Author : Wessex Institute of Technology
language : en
Publisher: WIT Press (UK)
Release Date : 2004
Computational Finance And Its Applications written by Wessex Institute of Technology and has been published by WIT Press (UK) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Business & Economics categories.
Intelligent computational systems have become increasingly important in many financial applications, such as portfolio selection, proprietary trading and risk management. At the same time, traditional techniques are constantly being improved and developed as a result of the increased power of modern computer systems.