R Programming For Machine Learning

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R Programming For Machine Learning

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Author : Peter Simon
language : en
Publisher: Peter Simon
Release Date :

R Programming For Machine Learning written by Peter Simon and has been published by Peter Simon this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Master Machine Learning Using R — Build Powerful Predictive Models with Confidence! Are you eager to unlock the potential of machine learning in R Studio but unsure how to start? Whether you’re a complete beginner or an analyst looking to expand your skills, R Programming for Machine Learning is the perfect guide to learn how to apply machine learning techniques using the versatile R programming language. This book offers a comprehensive introduction to using R for machine learning, covering essential algorithms and real-world examples that empower you to build accurate predictive models. What You’ll Gain from This Machine Learning Course in R: Learn R Programming in Machine Learning Context Discover why the R language for machine learning is widely favored for data science, and how to leverage it to build models. Supervised Learning Techniques in R Master regression and classification methods to predict outcomes and categorize data efficiently. Unsupervised Learning in R Studio Explore clustering and pattern detection with hands-on examples. Model Validation and Optimization Learn how to evaluate your models and improve performance using practical techniques. Step-by-Step R Programming Classes Designed for learners who want a structured R programming course focused on machine learning applications. Why Choose This Book? 🧠 Ideal for Beginners and Intermediate Learners — Whether you want to learn R programming from scratch or deepen your knowledge of machine learning with R. 💻 Practical and Hands-On — Learn by doing, with examples and projects that bring R coding concepts to life. 📊 Focus on Data Science with R — Build skills that are in high demand across industries. 🎓 Comprehensive Learning Path — From basics to advanced topics in an easy-to-follow format that mimics a top-tier machine learning in R course. Who Should Get This Book? Beginners eager to learn R language for data science and machine learning Data analysts and scientists looking to apply R programming in machine learning Students taking machine learning course in R or machine learning with R course online Anyone passionate about mastering the R programming language for AI and predictive analytics Start building intelligent models today with the power of R programming for machine learning. 🛒 Order your copy now and transform your data skills with R!

Practical Machine Learning In R

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Author : Fred Nwanganga
language : en
Publisher: John Wiley & Sons
Release Date : 2020-04-10

Practical Machine Learning In R written by Fred Nwanganga 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 2020-04-10 with Computers categories.

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.

Learn R Programming In 24 Hours

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Author : Alex Nordeen
language : en
Publisher: Guru99
Release Date : 2020-10-31

Learn R Programming In 24 Hours written by Alex Nordeen and has been published by Guru99 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Computers categories.

R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?

R Programming

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Author : Daniel Daniel Bell
language : en
Publisher:
Release Date : 2020-06

R Programming written by Daniel Daniel Bell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06 with categories.

2020- Second Edition, updated version. Get your copy!! Why Learn R? What are the reasons to learn R for Data Science? In this small textbook recommended for absolute beginners, the author tried to answer these types of questions. After reading this guide, maybe in one or two days, you will learn the basics of R programming and its utility for each Data Scientist. Indeed, R is a programming language and software environment for statistical analysis, graphics representation, and reporting. If you are trying to understand the R programming language as a beginner, this short book will give you enough understanding of almost all the concepts of the R language. The author will guide you through examples, how to program in R and how to use R for effective data analysis.Get your copy Now! Book Objectives This book is about R programming. The following are the objectives of the author: To familiarize you with the basics of R programming language. To help you understand the various fields where R can be applied and its use cases in each field. To equip you with R programming skills, both beginner and advanced skills. To introduce you to R programming for data analysis. To introduce you to R programming for machine learning. To help you understand and appreciate the power of R in statistical computing, data analysis, and scientific research. Who this Book is for? Anybody who is a complete beginner to R Programming. Anybody in need of advancing their R Programming skills. Professionals in computer programming. Professors, lecturers or tutors who are looking to find better ways to explain R programming to their students in the simplest and easiest way. Students and academicians, especially those focusing on R, Data Analysis,Machine Learning , computer science, and Databases development. Requirements The author expects you to have a computer installed with an operating system such as Linux, Windows or Mac OS X. What is inside the book? R BASICS R DATA TYPES R VARIABLES AND CONSTANTS R OPERATORS DECISION MAKING IN R R LOOPS R FUNCTIONS R CLASSES AND OBJECTS R FOR DATA SCIENCE R FOR MACHINE LEARNING From the Back Cover. R programming language is one of the most popular languages used by statisticians, data analysts, researchers to retrieve, clean, analyze, visualize and present data. This is a comprehensive book on how to get started with R programming, why you should learn it and how you can learn it. Daniel Bell begins by introducing the readers to the foundations of the R programming language. The aim is to help you understand, how the R interpreter works, the origin of the name R, how to set up the R programming environment, etc. The author has discussed the process of installing R on Windows, Linux and Mac OS. Moreover, the author has explored the basics of R programming including writing comments, using the R console, creating R script files, etc. The various features provided by R have been discussed in depth, including data types, variables, loops, decision making, functions, operators, classes, and objects, etc. The author has also discussed R for data science and R for machine learning. The book has been organized into chapters, with each chapter having many sub-chapters. R code scripts have been provided, alongside thorough explanations of the code and images showing the expected output upon the execution of every script. Get your copy

R Programming For Ai

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Author : Peter Simon
language : en
Publisher: Peter Simon
Release Date :

R Programming For Ai written by Peter Simon and has been published by Peter Simon this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

R Programming for AI: Learn to Build Intelligent Models, Analyze Data, and Unlock AI's Full Potential Using R’s Cutting-Edge Tools and Libraries Discover How to Use R for Artificial Intelligence—From Machine Learning to Neural Networks, All in One Powerful Language Are you ready to combine the worlds of artificial intelligence and data science with R? Whether you're a data analyst, programmer, or student, R Programming for AI gives you the hands-on skills to build intelligent systems using the versatile and powerful R programming language. This book is your ultimate guide to mastering R for artificial intelligence, teaching you how to harness the full potential of the R language for AI projects—from training machine learning models to building neural networks and working with natural language processing. 🚀 What You’ll Learn Inside This R Programming Course: ✅ How to Use R for Machine Learning Create predictive models using powerful R packages like caret, randomForest, and xgboost. This is R programming training you can apply instantly. ✅ Deep Learning with R Explore neural networks using keras and tensorflow in R. Learn to build AI models capable of solving complex tasks like image and speech recognition. ✅ Natural Language Processing with R Language Analyze text, build sentiment analysis engines, and apply NLP with R’s advanced text mining packages—perfect for real-world AI challenges. ✅ Data Analysis and Visualization Combine R coding and AI techniques to manipulate, explore, and visualize data in ways that drive smart decisions. ✅ Real Projects, Real Results Each chapter includes a hands-on project to reinforce your skills. You'll work on fraud detection, customer segmentation, and more. 💡 Why Choose the R Language for AI? 🔧 R is a Programming Language Built for Data – Unlike other languages, R was made for statistical computing and data modeling, making it ideal for AI applications. 🌐 All-in-One Solution – No need to jump between platforms. The R program software environment gives you everything: coding, analysis, modeling, and visualization. 🧠 Perfect for Analysts, Scientists & Developers – Whether you're new to AI or looking to expand your skill set, this book blends R development language expertise with AI best practices. 🎯 Who This R Course is For: Programmers ready to learn R programming for artificial intelligence Students seeking a practical R programming language course that includes AI applications Data analysts looking to upskill with an R language course focused on machine learning Self-learners who prefer a book-based R coding program over expensive online courses Anyone ready to apply language R programming to real-world intelligent systems 🎁 Bonus Content You’ll Get: Ready-to-run R code programming scripts and AI templates Sample datasets for hands-on practice Access to resources via our R programming website Tips and tricks to get the most from the R computer language in AI workflows ✅ Don’t Just Analyze Data—Make It Smarter with AI and R If you’ve been searching for the perfect way to learn R while also mastering AI, this is the guide for you. More than just theory, this book delivers practical R programming classes in AI that you can implement immediately. So whether you're looking to enroll in an R language online course, build your own AI models, or take your data career to the next level, this book is your first step. 🛒 Scroll up and grab your copy of R Programming for AI now—and turn data into intelligent action using the full power of R!

R Programming

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Author : Daniel Bell
language : en
Publisher:
Release Date : 2019-09-30

R Programming written by Daniel Bell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-30 with categories.

R is a programming language and software environment for statistical analysis, graphics representation, and reporting. If you are trying to understand the R programming language as a beginner, this short book will give you enough understanding of almost all the concepts of the R language. The author will guide you through examples, how to program in R and how to use R for effective data analysis.Buy your copy Now! Book ObjectivesThis book is about R programming. The following are the objectives of the author: To familiarize you with the basics of R programming language. To help you understand the various fields where R can be applied and its use cases in each field. To equip you with R programming skills, both beginner and advanced skills. To introduce you to R programming for data analysis. To introduce you to R programming for machine learning. To help you understand and appreciate the power of R in statistical computing, data analysis, and scientific research. Who this Book is for? Anybody who is a complete beginner to R Programming. Anybody in need of advancing their R Programming skills. Professionals in computer programming. Professors, lecturers or tutors who are looking to find better ways to explain R programming to their students in the simplest and easiest way. Students and academicians, especially those focusing on R, Data Analysis, Machine Learning, computer science, and Databases development. RequirementsThe author expects you to have a computer installed with an operating system such as Linux, Windows or Mac OS X. What is inside the book? R BASICS R DATA TYPES R VARIABLES AND CONSTANTS R OPERATORS DECISION MAKING IN R R LOOPS R FUNCTIONS R CLASSES AND OBJECTS R FOR DATA SCIENCE R FOR MACHINE LEARNING From the Back Cover.R programming language is one of the most popular languages used by statisticians, data analysts, researchers to retrieve, clean, analyze, visualize and present data. This is a comprehensive book on how to get started with R programming, why you should learn it and how you can learn it. Daniel Bell begins by introducing the readers to the foundations of the R programming language. The aim is to help you understand, how the R interpreter works, the origin of the name R, how to set up the R programming environment, etc. The author has discussed the process of installing R on Windows, Linux and Mac OS. Moreover, the author has explored the basics of R programming including writing comments, using the R console, creating R script files, etc. The various features provided by R have been discussed in depth, including data types, variables, loops, decision making, functions, operators, classes, and objects, etc. The author has also discussed R for data science and R for machine learning. The book has been organized into chapters, with each chapter having many sub-chapters. R code scripts have been provided, alongside thorough explanations of the code and images showing the expected output upon the execution of every script.

Introduction To Machine Learning With R

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Author : Scott Burger
language : en
Publisher:
Release Date : 2018

Introduction To Machine Learning With R written by Scott Burger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with R (Computer program language) categories.

Machine learning can be a difficult subject if you’re not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You’ll start with the basics like regression, then move into more advanced topics like neural networks, and finally delve into the frontier of machine learning in the R world with packages like Caret. By developing a familiarity with topics like understanding the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Knowing when to use a specific model or not can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning. Understand the major parts of machine learning algorithms Recognize how machine learning can be used to solve a problem in a simple manner Figure out when to use certain machine learning algorithms versus others Learn how to operationalize algorithms with cutting edge packages

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).

Machine Learning Using R

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Author : Karthik Ramasubramanian
language : en
Publisher: Apress
Release Date : 2019-01-04

Machine Learning Using R written by Karthik Ramasubramanian and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-04 with Computers categories.

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.

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