Mathematics And Programming For Machine Learning With R

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Mathematics And Programming For Machine Learning With R

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Author : William Claster
language : en
Publisher: CRC Press
Release Date : 2020-10-26

Mathematics And Programming For Machine Learning With R written by William Claster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-26 with Computers categories.

Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R. The book begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to the coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with machine learning based on artificial neural networks. The first half of the book does not require mathematical sophistication, although familiarity with probability and statistics would be helpful. The second half assumes the reader is familiar with at least one semester of calculus. The text guides novice R programmers through algorithms and their application and along the way; the reader gains programming confidence in tackling advanced R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of machine learning algorithms

Mathematics And R Programming For Machine Learning

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Author : William B. Claster
language : en
Publisher: CRC Press
Release Date : 2020-10-08

Mathematics And R Programming For Machine Learning written by William B. Claster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-08 with categories.

Based on the author's experience teaching data science for more than 10 years, Mathematics and R Programming for Machine Learningreveals how machine learning algorithms do their magic and explains how logic can be implemented in code. It is designed to give students an understanding of the logic behind machine learning algorithms as well as how to program these algorithms. Written for novice programmers, the book goes step-by-step to develop coding skills needed to implement algorithms in R. The text begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with artificial neural network-based machine learning. The first half of the text does not require mathematical sophistication, although familiarity with probability and statistics is helpful. The second half is written for students who have taken one semester of calculus. The book guides students, who are novice R programmers, through algorithms and their application to improve the ability to code and confidence in programming R and tackling advance R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners on implementing full-fledged algorithms. Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of the heart of AI and machine learning as well as the mechanisms that underly machine learning algorithms

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Advanced R Statistical Programming And Data Models

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Author : Matt Wiley
language : en
Publisher: Apress
Release Date : 2019-02-20

Advanced R Statistical Programming And Data Models written by Matt Wiley and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-20 with Computers categories.

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).

Just Enough R

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Author : Richard J. Roiger
language : en
Publisher: CRC Press
Release Date : 2020-05-20

Just Enough R written by Richard J. Roiger and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-20 with Computers categories.

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool Uses RStudio’s integrated development environment Shows how to interface R with SQLite Includes examples using R’s Rattle graphical user interface Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts Includes datasets from several areas, including business, health and medicine, and science About the Author Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.

Mastering Machine Learning With R

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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-31

Mastering Machine Learning With R written by Cory Lesmeister 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 2019-01-31 with Computers categories.

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications Key FeaturesBuild independent machine learning (ML) systems leveraging the best features of R 3.5Understand and apply different machine learning techniques using real-world examplesUse methods such as multi-class classification, regression, and clusteringBook Description Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work. What you will learnPrepare data for machine learning methods with easeUnderstand how to write production-ready code and package it for useProduce simple and effective data visualizations for improved insightsMaster advanced methods, such as Boosted Trees and deep neural networksUse natural language processing to extract insights in relation to textImplement tree-based classifiers, including Random Forest and Boosted TreeWho this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Introduction To Deep Learning Using R

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Author : Taweh Beysolow II
language : en
Publisher: Apress
Release Date : 2017-07-19

Introduction To Deep Learning Using R written by Taweh Beysolow II and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-19 with Computers categories.

Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn Understand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.

Applied Data Science With R For Ai And Machine Learning

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Author : Dr S Alex David
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-05-17

Applied Data Science With R For Ai And Machine Learning written by Dr S Alex David and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-17 with Computers categories.

Dr S Alex David, Professor and Head, Department of Artificial Intelligence and Machine Learning, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India. Dr.Konda Hari Krishna, Associate Professor, Department of Computer Science & Engineering, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India. Mrs.A.Bharathi, Assistant Professor, Department of Computer Science, Audisankara College of Engineering and Technology (Autonoumous), Gudur, Tirupati, Andhra Pradesh, India. Dr Almas Begum, Professor, Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India.

R Machine Learning Projects

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Author : Dr. Sunil Kumar Chinnamgari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-14

R Machine Learning Projects written by Dr. Sunil Kumar Chinnamgari 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 2019-01-14 with Mathematics categories.

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.5Build intelligent end-to-end projects for finance, retail, social media, and a variety of domainsImplement smart cognitive models with helpful tips and best practicesBook Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learnExplore deep neural networks and various frameworks that can be used in RDevelop a joke recommendation engine to recommend jokes that match users’ tastesCreate powerful ML models with ensembles to predict employee attritionBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationWho this book is for If you’re a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.

Statistical Learning With Math And Python

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Author : Joe Suzuki
language : en
Publisher: Springer Nature
Release Date : 2021-08-03

Statistical Learning With Math And Python written by Joe Suzuki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-03 with Computers categories.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.