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Unveiling Machine Learning: Theory, Algorithms and Practical Applications

Author: Dr.Padmaja Pulicherla

Publisher: SK Research Group of Companies

Published: 2024-05-02

Total Pages: 221

ISBN-13: 8119980727

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

Published: 2014-05-19

Total Pages: 415

ISBN-13: 1139952749

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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.

Machine Learning: Theoretical Foundations and Practical Applications

Author: Manjusha Pandey

Publisher: Springer Nature

Published: 2021-04-19

Total Pages: 172

ISBN-13: 9813365188

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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 Theory and Applications

Author: Xavier Vasques

Publisher: John Wiley & Sons

Published: 2024-01-11

Total Pages: 516

ISBN-13: 1394220626

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Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.

Machine Learning

Author: Mello

Publisher:

Published: 2021

Total Pages: 0

ISBN-13: 9789732346877

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Unlock the power of Statistical Learning Theory in Machine Learning with practical examples, algorithms, and source codes

Mastering Machine Learning Algorithms

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

Published: 2020-01-31

Total Pages: 799

ISBN-13: 1838821910

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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.

Information Theory, Inference and Learning Algorithms

Author: David J. C. MacKay

Publisher: Cambridge University Press

Published: 2003-09-25

Total Pages: 694

ISBN-13: 9780521642989

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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.

Algorithms

Author: Rob Botwright

Publisher: Rob Botwright

Published: 101-01-01

Total Pages: 286

ISBN-13: 1839386193

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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

Author:

Publisher: BoD – Books on Demand

Published: 2021-12-22

Total Pages: 153

ISBN-13: 183969484X

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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

Published: 2022-09-29

Total Pages: 212

ISBN-13: 1000737691

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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.