Unveiling Machine Learning Theory Algorithms And Practical Applications

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Unveiling Machine Learning: Theory, Algorithms and Practical Applications

Author : Dr.Padmaja Pulicherla
Publisher : SK Research Group of Companies
ISBN 13 : 8119980727
Total Pages : 221 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Unveiling Machine Learning: Theory, Algorithms and Practical Applications by : Dr.Padmaja Pulicherla

Download or read book Unveiling Machine Learning: Theory, Algorithms and Practical Applications written by Dr.Padmaja Pulicherla and published by SK Research Group of Companies. This book was released on 2024-05-02 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr.Padmaja Pulicherla, Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. Dr.Kasarla Satish Reddy, Professor, Department of Electronics and Communication Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. D.Satyanarayana, Assistant Professor, Department of Computer Science and Engineering(DS), Santhiram Engineering College(Autonomous), Nandyal, Andhra Pradesh, India. Dr.R.Sudheer Babu, Associate Professor, Department of Electronics and Communication Engineering, G.Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, India. Dr.Ravi Babu Devareddi, Assistant Professor, Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India.

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1139952749
Total Pages : 415 pages
Book Rating : 4.1/5 (399 download)

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Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Information Theory, Inference and Learning Algorithms

Author : David J. C. MacKay
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)

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Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Machine Learning: Theoretical Foundations and Practical Applications

Author : Manjusha Pandey
Publisher : Springer
ISBN 13 : 9789813365209
Total Pages : 172 pages
Book Rating : 4.3/5 (652 download)

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Book Synopsis Machine Learning: Theoretical Foundations and Practical Applications by : Manjusha Pandey

Download or read book Machine Learning: Theoretical Foundations and Practical Applications written by Manjusha Pandey and published by Springer. This book was released on 2022-04-21 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.

Machine Learning

Author :
Publisher : BoD – Books on Demand
ISBN 13 : 183969484X
Total Pages : 153 pages
Book Rating : 4.8/5 (396 download)

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Book Synopsis Machine Learning by :

Download or read book Machine Learning written by and published by BoD – Books on Demand. This book was released on 2021-12-22 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning

Author : Seyedeh Leili Mirtaheri
Publisher : CRC Press
ISBN 13 : 1000737691
Total Pages : 212 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Machine Learning by : Seyedeh Leili Mirtaheri

Download or read book Machine Learning written by Seyedeh Leili Mirtaheri and published by CRC Press. This book was released on 2022-09-29 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary, this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms, covers state-of-the-art, practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications.

Inside Deep Learning

Author : Edward Raff
Publisher : Simon and Schuster
ISBN 13 : 1638357218
Total Pages : 598 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Inside Deep Learning by : Edward Raff

Download or read book Inside Deep Learning written by Edward Raff and published by Simon and Schuster. This book was released on 2022-07-05 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English. About the technology Deep learning doesn’t have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don’t have to be a mathematics expert or a senior data scientist to grasp what’s going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. About the book Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You’ll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! What's inside Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology About the reader For Python programmers with basic machine learning skills. About the author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. Table of Contents PART 1 FOUNDATIONAL METHODS 1 The mechanics of learning 2 Fully connected networks 3 Convolutional neural networks 4 Recurrent neural networks 5 Modern training techniques 6 Common design building blocks PART 2 BUILDING ADVANCED NETWORKS 7 Autoencoding and self-supervision 8 Object detection 9 Generative adversarial networks 10 Attention mechanisms 11 Sequence-to-sequence 12 Network design alternatives to RNNs 13 Transfer learning 14 Advanced building blocks

Algorithms

Author : Rob Botwright
Publisher : Rob Botwright
ISBN 13 : 1839386193
Total Pages : 286 pages
Book Rating : 4.8/5 (393 download)

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Book Synopsis Algorithms by : Rob Botwright

Download or read book Algorithms written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing "ALGORITHMS: COMPUTER SCIENCE UNVEILED" - Your Path to Algorithmic Mastery! Are you fascinated by the world of computer science and the magic of algorithms? Do you want to unlock the power of algorithmic thinking and take your skills to expert levels? Look no further! This exclusive book bundle is your comprehensive guide to mastering the art of algorithms and conquering the exciting realm of computer science. 📘 BOOK 1 - COMPUTER SCIENCE: ALGORITHMS UNVEILED 📘 · Dive into the fundamentals of algorithms. · Perfect for beginners and those new to computer science. · Learn the building blocks of algorithmic thinking. · Lay a strong foundation for your journey into the world of algorithms. 📘 BOOK 2 - MASTERING ALGORITHMS: FROM BASICS TO EXPERT LEVEL 📘 · Take your algorithmic skills to new heights. · Explore advanced sorting and searching techniques. · Uncover the power of dynamic programming and greedy algorithms. · Ideal for students and professionals looking to become algorithmic experts. 📘 BOOK 3 - ALGORITHMIC MASTERY: A JOURNEY FROM NOVICE TO GURU 📘 · Embark on a transformative journey from novice to guru. · Master divide and conquer strategies. · Discover advanced data structures and their applications. · Tackle algorithmic challenges that demand mastery. · Suitable for anyone seeking to elevate their problem-solving abilities. 📘 BOOK 4 - ALGORITHMIC WIZARDRY: UNRAVELING COMPLEXITY FOR EXPERTS 📘 · Push the boundaries of your algorithmic expertise. · Explore expert-level techniques and conquer puzzles. · Unleash the full power of algorithmic mastery. · For those who aspire to become true algorithmic wizards. Why Choose "ALGORITHMS: COMPUTER SCIENCE UNVEILED"? 🌟 Comprehensive Learning: Covering the entire spectrum of algorithmic knowledge, this bundle caters to beginners and experts alike. 🌟 Progression: Start with the basics and gradually advance to expert-level techniques, making it accessible for learners at all stages. 🌟 Real-World Application: Gain practical skills and problem-solving abilities that are highly sought after in the world of computer science. 🌟 Expert Authors: Written by experts in the field, each book provides clear explanations and hands-on examples. 🌟 Career Advancement: Enhance your career prospects with a deep understanding of algorithms, an essential skill in today's tech-driven world. Unlock the Secrets of Computer Science Today! Whether you're a student, a professional, or simply curious about computer science, "ALGORITHMS: COMPUTER SCIENCE UNVEILED" is your gateway to a world of knowledge and expertise. Don't miss this opportunity to acquire a valuable skill set that can propel your career to new heights. Get your copy now and embark on a journey to algorithmic mastery!

Machine Learning Algorithms and Applications

Author : Mettu Srinivas
Publisher : John Wiley & Sons
ISBN 13 : 1119769248
Total Pages : 372 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Machine Learning Algorithms and Applications by : Mettu Srinivas

Download or read book Machine Learning Algorithms and Applications written by Mettu Srinivas and published by John Wiley & Sons. This book was released on 2021-08-10 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Theory and Novel Applications of Machine Learning

Author : Er Meng Joo
Publisher : BoD – Books on Demand
ISBN 13 : 3902613556
Total Pages : 390 pages
Book Rating : 4.9/5 (26 download)

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Book Synopsis Theory and Novel Applications of Machine Learning by : Er Meng Joo

Download or read book Theory and Novel Applications of Machine Learning written by Er Meng Joo and published by BoD – Books on Demand. This book was released on 2009-01-01 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even since computers were invented, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science. Machine Learning (ML) draws upon ideas from a diverse set of disciplines, including AI, Probability and Statistics, Computational Complexity, Information Theory, Psychology and Neurobiology, Control Theory and Philosophy. ML involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc. Real-world applications of ML are widespread such as Pattern Recognition, Data Mining, Gaming, Bio-science, Telecommunications, Control and Robotics applications. This books reports the latest developments and futuristic trends in ML.

Machine Learning Refined

Author : Jeremy Watt
Publisher : Cambridge University Press
ISBN 13 : 1107123526
Total Pages : 301 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Machine Learning Refined by : Jeremy Watt

Download or read book Machine Learning Refined written by Jeremy Watt and published by Cambridge University Press. This book was released on 2016-09-08 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new, intuitive approach to machine learning, covering fundamental concepts and real-world applications, with practical MATLAB-based exercises.

Machine Learning

Author : Kamal Kant Hiran
Publisher : BPB Publications
ISBN 13 : 9391392350
Total Pages : 309 pages
Book Rating : 4.3/5 (913 download)

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Book Synopsis Machine Learning by : Kamal Kant Hiran

Download or read book Machine Learning written by Kamal Kant Hiran and published by BPB Publications. This book was released on 2021-09-16 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concepts of Machine Learning with Practical Approaches. KEY FEATURES ● Includes real-scenario examples to explain the working of Machine Learning algorithms. ● Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. ● Full of Python codes, numerous exercises, and model question papers for data science students. DESCRIPTION The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems. WHAT YOU WILL LEARN ● Perform feature extraction and feature selection techniques. ● Learn to select the best Machine Learning algorithm for a given problem. ● Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. ● Learn about Artificial Neural Network along with the Back Propagation Algorithm. ● Make use of various recommended systems with powerful algorithms. WHO THIS BOOK IS FOR This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cases. Knowing basic statistical and programming concepts would be good, although not mandatory. TABLE OF CONTENTS 1. Introduction 2. Supervised Learning Algorithms 3. Unsupervised Learning 4. Introduction to the Statistical Learning Theory 5. Semi-Supervised Learning and Reinforcement Learning 6. Recommended Systems

Machine Learning

Author : Jugal Kalita
Publisher : CRC Press
ISBN 13 : 100081825X
Total Pages : 311 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Machine Learning by : Jugal Kalita

Download or read book Machine Learning written by Jugal Kalita and published by CRC Press. This book was released on 2022-12-21 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.

Ensemble Machine Learning

Author : Cha Zhang
Publisher : Springer Science & Business Media
ISBN 13 : 1441993266
Total Pages : 332 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Ensemble Machine Learning by : Cha Zhang

Download or read book Ensemble Machine Learning written by Cha Zhang and published by Springer Science & Business Media. This book was released on 2012-02-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Machine Learning

Author : Mohssen Mohammed
Publisher : CRC Press
ISBN 13 : 9780367574673
Total Pages : 204 pages
Book Rating : 4.5/5 (746 download)

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Book Synopsis Machine Learning by : Mohssen Mohammed

Download or read book Machine Learning written by Mohssen Mohammed and published by CRC Press. This book was released on 2020-06-30 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are new to machine learning and you do not know where to start, then the answer is this book. If you know some of the theories in machine learning, but you do not know how to write your own algorithms, then again you should start with this book. This book explains the concepts of machine learning algorithms and provides simple, practical examples to help you understand each algorithm. This book focuses on supervised and unsupervised machine learning methods. The main objective of this book is to introduce these methods in a simple and practical way so that even someone new to the topics can understand and benefit from them. The book consists of 12 chapters divided into two sections: supervised learning algorithms and unsupervised learning algorithms. In supervised learning, the target is to infer a function or mapping from training data that is labeled. In unsupervised learning, we lack supervisors or training data. In other words, all we have is unlabeled data. The idea is to find a hidden structure in this data. Section I on supervised learning algorithms discusses decision trees, rule-based classifiers, naïve Bayes classification, k-nearest neighbors, neural networks, linear discriminant analysis, and support vector machines. Section II on unsupervised learning algorithms discusses k-means, Gaussian mixture model, hidden Markov model, and principal component analysis in the context of dimensionality reduction. The chapters are written independent of one another, so you can start from any chapter and understand it easily. Each chapter discusses the algorithms through which the chapter methods work and implements the algorithms in MATLAB®. Book jacket.

A Course in Machine Learning

Author : H Daume
Publisher :
ISBN 13 : 9789732346846
Total Pages : 0 pages
Book Rating : 4.3/5 (468 download)

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Book Synopsis A Course in Machine Learning by : H Daume

Download or read book A Course in Machine Learning written by H Daume and published by . This book was released on 2023-08-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embark on an exhilarating journey into the realm of modern technological marvels with this comprehensive guide. Unveil the power of algorithms that can discern patterns in vast troves of data, opening doors to innovation and insight. Whether you're a tech enthusiast, a curious mind, or a seasoned programmer, "A Course in Machine Learning" invites you to demystify the enigmatic world of AI and data science. Within these pages, you'll unravel the intricacies of machine learning, guided by a seasoned expert who brings theory to life with real-world examples. Explore the algorithms that lie at the heart of self-driving cars, virtual assistants, and predictive analytics. Through hands-on exercises, sharpen your skills in creating intelligent systems that adapt and learn from experience. Dive into the realm of neural networks and deep learning, where layers of interconnected neurons mimic the human brain's astonishing capabilities. Grasp the art of feature engineering and data preprocessing to distill meaningful insights from noisy data. With step-by-step tutorials, you'll seamlessly transition from theory to practice, developing models that can decipher handwritten text, identify objects in images, and even predict future trends. Unlock the potential of unsupervised learning and reinforcement learning, letting algorithms uncover hidden patterns and optimize decision-making processes. From healthcare to finance, from entertainment to agriculture, the applications of machine learning are limitless. Gain the confidence to tackle real-world challenges and harness the power of data to transform industries and shape the future. Join the ranks of innovators who are reshaping our world through machine learning's unprecedented possibilities. Whether you're a student, a professional, or simply an inquisitive mind, "A Course in Machine Learning" equips you with the tools to unravel the complexities of AI and build a future that's driven by intelligence and imagination. Experience the thrill of discovery as you journey through these pages, guided by the wisdom of a true trailblazer in the field.

Machine Learning Mastery: Algorithms and Applications

Author : Michael Roberts
Publisher : Richards Education
ISBN 13 :
Total Pages : 156 pages
Book Rating : 4./5 ( download)

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Book Synopsis Machine Learning Mastery: Algorithms and Applications by : Michael Roberts

Download or read book Machine Learning Mastery: Algorithms and Applications written by Michael Roberts and published by Richards Education. This book was released on with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the power of machine learning with Machine Learning Mastery: Algorithms and Applications. This comprehensive guide covers everything from fundamental concepts to advanced techniques, providing a deep dive into the algorithms that power modern AI and their practical applications across various industries. Whether you're a beginner looking to get started or an experienced practitioner seeking to deepen your knowledge, this book offers a structured and detailed exploration of data preprocessing, supervised and unsupervised learning, reinforcement learning, and deep learning. Learn how to evaluate and optimize models, deploy machine learning solutions, and navigate the ethical and practical challenges of implementing AI in the real world. With case studies and hands-on examples, Machine Learning Mastery is your essential companion on the journey to becoming a proficient machine learning expert.