Machine Learning Algorithms
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Basic Guide For Machine Learning Algorithms And Models
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Author : Ms.G.Vanitha
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-07-10
Basic Guide For Machine Learning Algorithms And Models written by Ms.G.Vanitha and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-10 with Computers categories.
Ms.G.Vanitha, Associate Professor, Department of Information Technology, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India. Dr.M.Kasthuri, Associate Professor, Department of Computer Science, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India.
Machine Learning
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2021-12-22
Machine Learning written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-22 with Computers categories.
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.
Encyclopedia Of Machine Learning
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Author : Claude Sammut
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28
Encyclopedia Of Machine Learning written by Claude Sammut and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Computers categories.
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
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 Algorithms
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Author : Dr. V S Nishok
language : en
Publisher: RK Publication
Release Date : 2024-12-02
Machine Learning Algorithms written by Dr. V S Nishok and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Computers categories.
Machine Learning Algorithms the fundamental principles, techniques, and applications of machine learning. Covering a wide range of algorithms, from supervised and unsupervised learning to reinforcement learning, the provides in-depth explanations of key models, including decision trees, neural networks, and support vector machines. It practical implementations, optimization techniques, and real-world applications across various domains. With a focus on both theoretical foundations and hands-on practice, this serves as an essential resource for students, researchers, and professionals seeking to develop a strong understanding of machine learning algorithms and their impact on modern technology.
Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-24
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 2017-07-24 with Computers categories.
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
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.
A Primer To The 42 Most Commonly Used Machine Learning Algorithms With Code Samples
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Author : Murat Durmus
language : en
Publisher: Murat Durmus
Release Date : 2023-02-01
A Primer To The 42 Most Commonly Used Machine Learning Algorithms With Code Samples written by Murat Durmus and has been published by Murat Durmus this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-01 with Computers categories.
Would you like a quick, profound overview of the most popular machine-learning algorithms? Then this is the book for you.! (This book is also suitable for Beginners) This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Each algorithm is also demonstrated with a simple code example in Python. About the Author Murat Durmus is CEO and founder of AISOMA (a Frankfurt am Main (Germany) based company specializing in AI-based technology development and consulting) and Author of the book "Mindful AI - Reflections on Artificial Intelligence" and "INSIDE ALAN TURING." The following algorithms are covered in this book: • ADABOOST • ADAM OPTIMIZATION • AGGLOMERATIVE CLUSTERING • ARMA/ARIMA MODEL • BERT • CONVOLUTIONAL NEURAL NETWORK • DBSCAN • DECISION TREE • DEEP Q-LEARNING • EFFICIENTNET • FACTOR ANALYSIS OF CORRESPONDENCES • GAN • GMM • GPT-3 • GRADIENT BOOSTING MACHINE • GRADIENT DESCENT • GRAPH NEURAL NETWORKS • HIERARCHICAL CLUSTERING • HIDDEN MARKOV MODEL (HMM) • INDEPENDENT COMPONENT ANALYSIS • ISOLATION FOREST • K-MEANS • K-NEAREST NEIGHBOUR • LINEAR REGRESSION • LOGISTIC REGRESSION • LSTM • MEAN SHIFT • MOBILENET • MONTE CARLO ALGORITHM • MULTIMODAL PARALLEL NETWORK • NAIVE BAYES CLASSIFIERS • PROXIMAL POLICY OPTIMIZATION • PRINCIPAL COMPONENT ANALYSIS • Q-LEARNING • RANDOM FORESTS • RECURRENT NEURAL NETWORK • RESNET • SPATIAL TEMPORAL GRAPH CONVOLUTIONAL NETWORKS • STOCHASTIC GRADIENT DESCENT • SUPPORT VECTOR MACHINE • WAVENET • XGBOOST
Machine Learning Algorithms Handbook
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Author : Aman Kharwal
language : en
Publisher:
Release Date : 2023-09-15
Machine Learning Algorithms Handbook written by Aman Kharwal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-15 with Computers categories.
Key Features: Clear Explanations of Machine Learning Algorithms: The book offers clear and concise explanations of machine learning algorithms, ensuring that readers of all levels can grasp the concepts effortlessly. Hands-On Approach: Packed with practical examples using Python and code snippets, you'll gain a hands-on understanding of how each algorithm works and learn to implement them in real projects. Comprehensive Coverage: From linear regression and support vector machines to decision trees and neural networks, the book covers a wide array of algorithms, giving you a solid foundation to explore diverse problem domains. Performance Evaluation Methods: Learn how to evaluate the effectiveness of your models, identify areas for improvement, and optimize their performance using industry-standard evaluation techniques. Data Preprocessing Techniques: Discover the critical elements of data preprocessing that lay the groundwork for building robust and accurate machine learning models. Time Series Forecasting: Explore advanced algorithms specifically designed for time series data, a critical component of numerous real-world applications. Appendix for Easy Reference: Access all parameters of commonly used machine learning algorithms in a handy appendix, facilitating efficient model tuning.
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!