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.

Download or read book Machine Learning Theoretical Foundations and Practical Applications written by Manjusha Pandey and published by Springer Nature. This book was released on 2021-04-19 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.

Download or read book Machine Learning Theory and Applications written by Xavier Vasques and published by John Wiley & Sons. This book was released on 2024-01-11 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning written by Mello and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the power of Statistical Learning Theory in Machine Learning with practical examples, algorithms, and source codes

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.

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!

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.

Download or read book Machine Learning written by Mohssen Mohammed and published by CRC Press. This book was released on 2016-08-19 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

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.

Download or read book Cracking The Code written by Max Harper and published by Max Harper. This book was released on 2024-07-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cracking the Code" unveils the secrets of machine learning algorithms, guiding you through the intricate world of artificial intelligence with clarity and depth. Whether you're an aspiring data scientist, a seasoned machine learning practitioner, or simply curious about the technology shaping our future, this book equips you with the knowledge and skills to harness the power of machine learning effectively. Comprehensive Coverage: Dive deep into the core concepts and algorithms of machine learning, from regression and classification to clustering and reinforcement learning. With clear explanations and real-world examples, this book demystifies complex topics, empowering you to understand how machine learning algorithms work and how to apply them to solve practical problems. Hands-On Practice: Put theory into practice with hands-on exercises and coding examples in Python. Gain practical experience implementing machine learning algorithms and techniques, and learn how to evaluate model performance, tune hyperparameters, and interpret results effectively. Advanced Techniques: Explore advanced topics in machine learning, including deep learning, ensemble methods, and natural language processing. Learn how to leverage cutting-edge techniques and algorithms to tackle complex challenges and extract meaningful insights from large datasets. Real-World Applications: Discover how machine learning is transforming industries and revolutionizing various fields, from healthcare and finance to marketing and cybersecurity. With case studies and examples drawn from diverse domains, this book illustrates the practical applications of machine learning and inspires you to explore new possibilities in your own work. Unlock Your Potential: Whether you're looking to advance your career, launch innovative projects, or simply deepen your understanding of machine learning, "Cracking the Code" provides the tools and knowledge you need to succeed. Empower yourself with the skills and insights to unlock the full potential of machine learning and shape the future of technology.

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.

Download or read book Machine Learning for Beginners written by Jason Knox and published by . This book was released on 2019-12-07 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thinking about beginning a career in the field of Data Science? Do you want to understand more in depth everything that concerns Machine Learning? Or maybe you're a total newbie eager to start learning this topic from zero or so. Machine Learning is one of the most exciting developments to come out of computer science since its founding. It's dramatically changing society all around us and the new occupation of Data Science which has arisen as a result of the development of Machine Learning has opened up a new career path that guarantees employment that is exciting, at the cutting edge, and guaranteed to be challenging. Maybe you're aware of all the hype but you are quite sure what Machine Learning is. If that's the case you've come to the right place. This book is designed to be a beginner's introduction to the exciting world of Machine Learning and Data Science. In this book we are going to pull the curtain back and reveal the secrets and tools used in these exciting fields. We'll begin by recounting a history of machines and how they are an extension of the human mind and also an extension of human labor. Then we will introduce you to the concept of Machine Learning and explore how it relates to Artificial Intelligence into Deep Learning. You will learn all the different ways that Machine Learning can be applied in the real world in practical circumstances. After this, we will reveal the different types of learning and training that is used in order to get computers to learn how to deal with the real world and become autonomous agents. We will teach you all about Supervised and Unsupervised Learning. You're also going to learn the concepts behind all the major algorithms that are used in Data Science and Machine Learning. Inside you'll discover: What Linear Regression is, and the concept of least squares; Types of learning used to train machines to think and act autonomously; Avoid getting lost in Decision Trees and Random Forests; Understand Logistic Regression; Learn how tools like Clustering are used; Find out some of the recent applications of Machine Learning to the real world; See how Machine Learning is being used in Social Media, Analysis, by Government and by companies like Amazon, Netflix and Google; And much more... So, don't waste anymore time and let's start your journey !! ***Scroll up and click the BUY NOW button***

Download or read book Chaos Unveiled written by Barrett Williams and published by Barrett Williams. This book was released on 2024-08-21 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: **Chaos Unveiled An Expedition into the Intricate World of Chaos Theory and Complex Systems** Unlock the mysteries of the universe's most captivating phenomenon with "Chaos Unveiled," an enlightening journey through chaos theory and complex systems. Crafted to engross and educate, this book serves as your definitive guide to understanding the unpredictable nature of the world around us. Dive into **Chapter 1** and explore the humble beginnings of chaos theory and its dramatic evolution into a pivotal scientific discipline. Discover key contributors and the milestones that have defined this groundbreaking field. Move into **Chapter 2**, where the intricate mathematics behind chaos comes to life. Understand nonlinear dynamics, sensitivity to initial conditions, and the enigmatic strange attractors that lead to seemingly random behaviors out of deterministic processes. In **Chapter 3**, delve into the mesmerizing world of fractals and self-similarity. From the beauty of fractal geometry to real-world applications, this chapter is a visual and intellectual feast. **Chapter 4** takes you into fluid dynamics, demystifying the complex phenomena of turbulence, vortices, and the iconic Lorenz attractor that revolutionized weather prediction and modeling. Explore the fascinating roles chaos plays in **biological systems** in **Chapter 5**, from population dynamics and cardiac rhythms to intricate ecosystems and food webs. Unravel the secrets of chaotic behavior in chemical reactions in **Chapter 6**, and discover how these principles govern both simple reactions and the complex chemistry of living organisms. Venture into **economic chaos** in **Chapter 7**, understanding stock market fluctuations, market bubbles, and the unpredictable nature of economic forecasting. The applications of chaos theory in **engineering** are unlocked in **Chapter 8**, revealing insights into control systems, structural analysis, and the ever-evolving field of robotics and machine learning. **Chapter 9** sheds light on chaos in social systems, from human behavior and social networks to the intricacies of urban planning. Experience medical breakthroughs in **Chapter 10**, where chaos theory enhances diagnostics, epidemiology, and our understanding of brain activity. Discover chaos in the vast expanse of **environmental science** and **astronomy** in **Chapters 11 and 12**, exploring climate change, earthquake prediction, orbital mechanics, and cosmic phenomena. **Chapter 13** provides groundbreaking insights into computational approaches, from numerical simulations to data analysis and pattern recognition. Reflect on the **philosophical implications** in **Chapter 14** as you weigh determinism against randomness and ponder the ethical considerations of predictability. Finally, look towards the **future of chaos theory** in **Chapter 15**, exploring emerging research areas, interdisciplinary collaborations, and the bright horizon of unanswered questions and challenges. "Chaos Unveiled" is not merely a book—it's an invitation to explore, question, and understand the beautifully intricate world of chaos theory. Get ready to have your mind expanded and your curiosity ignited!

Download or read book Practical Machine Learning written by Sunila Gollapudi and published by Packt Publishing Ltd. This book was released on 2016-01-30 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.

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.

Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.