Machine Learning Algorithms And Concepts
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An Introduction To Machine Learning
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Author : Gopinath Rebala
language : en
Publisher: Springer
Release Date : 2019-05-07
An Introduction To Machine Learning written by Gopinath Rebala and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-07 with Technology & Engineering categories.
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any priorknowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.
Machine Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Roland Bind
Release Date : 2018-09-07
Machine Learning For Beginners written by Steven Cooper and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-07 with Computers categories.
If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. ★★ Grab your copy today and learn ★★ ♦ The different types of learning algorithm that you can expect to encounter ♦ The numerous applications of machine learning ♦ The different types of machine learning and how they differ ♦ The best practices for picking up machine learning ♦ What languages and libraries to work with ♦ The future of machine learning ♦ The various problems that you can solve with machine learning algorithms ♦ And much more... Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish. Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know about Machine Learning!
Machine Learning Algorithms And Concepts
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Author : Sariya Ansari
language : en
Publisher: Notion Press
Release Date : 2023-09-13
Machine Learning Algorithms And Concepts written by Sariya Ansari and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-13 with Computers categories.
This book is for machine learning professional & aspiring data scientist who wanted to be established themselves as a machine learning engineer or data science professional. Machine Learning Algorithms & Concepts gives complete idea to begin the phase of machine learning professional. This can be referred as a great starting point to switch the career path from existing profession to a machine learning professional. The book covers all major algorithms, its concept, usage, and other miscellaneous concepts based on situation which helps to its reader to decide in which situation what to be used. This book serves as guide to prepare for interviews, exams, campus work as well as for industry professional. It also covers basic programming which gives fair idea to its reader to learn how to code for machine learning problem statement even if he is a beginner in coding.
Machine Learning
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Author : T V Geetha
language : en
Publisher: CRC Press
Release Date : 2023-05-17
Machine Learning written by T V Geetha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-17 with Business & Economics categories.
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
Machine Learning And Big Data
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Author : Uma N. Dulhare
language : en
Publisher: John Wiley & Sons
Release Date : 2020-09-01
Machine Learning And Big Data written by Uma N. Dulhare and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-01 with Computers categories.
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-25
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-25 with Computers categories.
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Machine Learning Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2011-07-31
Machine Learning Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-31 with Computers categories.
"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe
Machine Learning Mathematics
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Author : Samuel Hack
language : en
Publisher:
Release Date : 2021-01-08
Machine Learning Mathematics written by Samuel Hack and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-08 with categories.
Machine Learning
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Author : Samuel Hack
language : en
Publisher:
Release Date : 2020-12-04
Machine Learning written by Samuel Hack and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
Master the World of Machine Learning - Even if You're a Complete Beginner With This Incredible 2-in1 Bundle Are you an aspiring entrepreneur? Are you an amateur software developer looking for a break in the world of machine learning? Do you want to learn more about the incredible world of Machine Learning, and what it can do for you? Then keep reading. Machine learning is the way of the future - and breaking into this highly lucrative and ever-evolving field is a great way for your career, or business, to prosper. Inside this guide, you'll find simple, easy-to-follow explanations of the fundamental concepts behind machine learning, from the mathematical and statistical concepts to the programming behind them. With a wide range of comprehensive advice including machine learning models, neural networks, statistics, and much more, this guide is a highly effective tool for mastering this incredible technology. In book one, you'll learn: What is Artificial Intelligence Really, and Why is it So Powerful? Choosing the Right Kind of Machine Learning Model for You An Introduction to Statistics Reinforcement Learning and Ensemble Modeling "Random Forests" and Decision Trees In book two, you'll learn: Learn the Fundamental Concepts of Machine Learning Algorithms Understand The Four Fundamental Types of Machine Learning Algorithm Master the Concept of "Statistical Learning Learn Everything You Need to Know about Neural Networks and Data Pipelines Master the Concept of "General Setting of Learning" A Free Bonus And Much More! Covering everything you need to know about machine learning, now you can master the mathematics and statistics behind this field and develop your very own neural networks! Whether you want to use machine learning to help your business, or you're a programmer looking to expand your skills, this bundle is a must-read for anyone interested in the world of machine learning. So don't wait - it's never been easier to learn. Buy now to become a master of Machine Learning Today!
Fundamental Mathematical Concepts For Machine Learning In Science
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Author : Umberto Michelucci
language : en
Publisher: Springer Nature
Release Date : 2024-05-16
Fundamental Mathematical Concepts For Machine Learning In Science written by Umberto Michelucci and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Mathematics categories.
This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines—such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research. Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches.