Elements Of Data Science Machine Learning And Artificial Intelligence Using R

Author : Frank Emmert-Streib
Publisher : Springer Nature
ISBN 13 : 3031133390
Total Pages : 582 pages
Book Rating : 4.98/5 ( download)

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Download or read book Elements of Data Science, Machine Learning, and Artificial Intelligence Using R written by Frank Emmert-Streib and published by Springer Nature. This book was released on 2023-10-03 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

Author : Fred Nwanganga
Publisher : John Wiley & Sons
ISBN 13 : 1119591511
Total Pages : 464 pages
Book Rating : 4.11/5 ( download)

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Download or read book Practical Machine Learning in R written by Fred Nwanganga and published by John Wiley & Sons. This book was released on 2020-05-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.

Author : Dr.CARMEL MARY BELINDA.M.J
Publisher : SK Research Group of Companies
ISBN 13 : 8119980093
Total Pages : 319 pages
Book Rating : 4.93/5 ( download)

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Download or read book DATA SCIENCE WITH R PROGRAMMING written by Dr.CARMEL MARY BELINDA.M.J and published by SK Research Group of Companies. This book was released on 2024-02-07 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr.CARMEL MARY BELINDA.M.J, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.K.NATTAR KANNAN, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.R.GNANAJEYARAMAN, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Saveetha University, Chennai, India. Dr.U.ARUL, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.RAMA MOORTHY, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.

Author : Cory Lesmeister
Publisher : Packt Publishing Ltd
ISBN 13 : 1783984538
Total Pages : 400 pages
Book Rating : 4.34/5 ( download)

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Download or read book Mastering Machine Learning with R written by Cory Lesmeister and published by Packt Publishing Ltd. This book was released on 2015-10-28 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

Author : Rayan Wali
Publisher : Rayan Wali
ISBN 13 :
Total Pages : 605 pages
Book Rating : 4./5 ( download)

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Download or read book A Practical Guide to Artificial Intelligence and Data Analytics written by Rayan Wali and published by Rayan Wali. This book was released on 2021-06-12 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics touches on all of the fundamental topics you will need to understand deeper into machine learning and artificial intelligence research, literature, and practical applications with its four parts: Part I: Concept Instruction Part II: 8 Full-Length Case Studies Part III: 50+ Mixed Exercises Part IV: A Full-Length Assessment With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design skills.

Author : Dominic Lordy
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781720424604
Total Pages : 114 pages
Book Rating : 4.08/5 ( download)

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Download or read book Machine Learning with R written by Dominic Lordy and published by Createspace Independent Publishing Platform. This book was released on 2016-05-27 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (Will soon return to 25.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using R? If you are looking for a complete beginners guide to learn Machine Learning using R, in just a few hours, this book is for you. Machine Learning is the practice of transforming data into knowledge, and R is the most popular open-source programming language used for Machine Learning. In this book, we will learn how to use the principles of Machine Learning and the R programming language to answer day-to-day questions about your data. Finally, we'll learn how to make predictions with machine learning. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all important R and Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach Machine Learning, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in Machine Learning and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on Machine Learning What's Inside This Book? Introduction Basic Functions Linear Regression Machine Learning Algorithms Data with R Generating data Graphical functions Programming with R in Practice Opening the Black Box K-nearest Neighbors Neural Networks Trees and Forests Standard Linear Model Logistic Regression Support Vector Machine using R Frequently Asked Questions Help! I got an error, what did I do wrong? Useful References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Machine Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at https: //aisciences.lpages.co/ai-sciences-data-science-with-r/

Author : Graham J. Williams
Publisher : CRC Press
ISBN 13 : 1351647490
Total Pages : 322 pages
Book Rating : 4.96/5 ( download)

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Download or read book The Essentials of Data Science: Knowledge Discovery Using R written by Graham J. Williams and published by CRC Press. This book was released on 2017-07-28 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.

Author : Tadeusz A. Grzeszczyk
Publisher : Taylor & Francis
ISBN 13 : 1040094414
Total Pages : 89 pages
Book Rating : 4.19/5 ( download)

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Download or read book Artificial Intelligence and Project Management written by Tadeusz A. Grzeszczyk and published by Taylor & Francis. This book was released on 2024-05-01 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although some people had doubts about the usefulness of such solutions in the past, artificial intelligence (AI) plays a growing role in modern business. It can be expected that the interest in it will also lead to an increase in support for the planning, evaluation, and implementation of projects. In particular, the proper functioning of multifaceted evaluation methods has a crucial impact on the appropriate planning and execution of various projects, as well as the effective achievement of the organization’s goals. This book offers a presentation of the complex problems and challenges related to the development of AI in project management, proposes an integrated approach to knowledge-based evaluation, and indicates the possibilities of improving professional practical knowledge in this field. The unique contribution of this book is to draw attention to the possibilities resulting from conducting transdisciplinary research and drawing on the rich achievements in the field of research development on knowledge-based systems that can be used to holistically support the processes of planning, evaluation, and project management. The concept of the integrated approach to knowledge-based evaluation is presented and developed as a result of drawing inspiration mainly from the systems approach, generative AI, and selected mathematical models. Presented in a highly accessible manner, the book discusses mathematical tools in a simple way, which enables understanding of the content by readers across broad subject areas who may be not only participants in specialist training and university students but also practitioners, consultants, or evaluators. This book will be a valuable resource for academics and upper-level students, in particular, across project management-related fields, and of great interest to all those looking to understand the challenges and effectiveness of AI in business.

Author : Brad Boehmke
Publisher : CRC Press
ISBN 13 : 1000730433
Total Pages : 374 pages
Book Rating : 4.32/5 ( download)

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Download or read book Hands-On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Author : Karthik Ramasubramanian
Publisher : Apress
ISBN 13 : 1484242157
Total Pages : 712 pages
Book Rating : 4.55/5 ( download)

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Download or read book Machine Learning Using R written by Karthik Ramasubramanian and published by Apress. This book was released on 2018-12-12 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. What You'll Learn Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R Who This Book is For Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.