You're reading from R Machine Learning Projects

Product typeBook

Published inJan 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789807943

Edition1st Edition

Languages

Tools

H2O TensorFlow

Concepts

Machine Learning

Author (1)

Dr. Sunil Kumar Chinnamgari

Datasets

Each chapter of the book describes an ML project that solves a business problem using an ML algorithm or a set of algorithms that we attempt to learn in that specific chapter. The projects considered are from different domains ranging from health care, to banking and finance, and to robots. The business problems solved in the chapters that follow are carefully selected to demonstrate solving a close-to-real-world business use case. The datasets used for the problems are popular open datasets. This will help us not only to explore the solutions covered in this book but also to examine other solutions that are developed for the problem. The problem solved in each of the chapters enriches our experience by applying ML algorithms in various domains and helps us get an understanding of how to solve the business problems in various domains successfully.

You have been reading a chapter from

R Machine Learning Projects

Published in: Jan 2019Publisher: PacktISBN-13: 9781789807943

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

undefined

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $15.99/month. Cancel anytime

Author (1)

Dr. Sunil Kumar Chinnamgari

Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.
Read more about Dr. Sunil Kumar Chinnamgari

Other recommended products

Related to this chapter

R Data Analysis Projects

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it is one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. In this book, we show you just how to do that - with the help of practical implementations of real-world use cases.

BookNov 2017366 pages

Mastering Machine Learning with R

Machine learning is the field of Artificial Intelligence where we 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. This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2.

BookApr 2017420 pages

Hands-On Recommendation Systems with Python

Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies or groceries, goes a long way in defining user experience and enticing your customers to use and buy from your platform. This book teaches you to do just that.

BookJul 2018146 pages

R Deep Learning Projects

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll see how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks and LSTMs—and also see how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.

BookFeb 2018258 pages

Machine Learning with R Cookbook

The R language is a powerful open source functional programming language. At its core, R is a statistical language that provides impressive tools to analyze data and create high-level graphics. This book covers the basics of R by setting up a user-friendly programming environment and programming ETL in R. Data exploration examples are provided that demonstrate how powerful data visualisation and machine learning is in discovering hidden relationships. You will also explore air quality data, steps to fix the missing values and visualising the same. You will then dive into important machine learning topics, including data classification, regression, survival analysis, time series analysis, clustering association rule mining, and dimension reduction.This book will include the latest code and examples based on R 3.3 and above—updated for better computation, accuracy, and speed with R.

BookOct 2017572 pages

Mastering Machine Learning with R

Machine learning is a field of AI where we build systems that learn from data. This book explains complicated concepts with real-world applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints. Finally, it will walk you through topics such as text analysis, time series, and deep learning.

BookJan 2019354 pages

R Deep Learning Essentials

This book demonstrates how to use deep Learning in R for machine learning, image classification, and natural language processing. It covers topics such as convolutional networks, recurrent neural networks, transfer learning and deep learning in the cloud. By the end of this book, you will be able to apply deep learning to real-world projects.

BookAug 2018378 pages

R Data Analysis Cookbook

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book empowers you by showing you ways to use R to generate professional analysis reports. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.

BookSep 2017560 pages

The Deep Learning Workshop

With The Deep Learning Workshop, you’ll learn about essential deep learning concepts, such as image recognition, text embedding, and neural networks, all so that you can build your own smart machine learning models. You'll be able to learn at your own pace with the help of interesting activities and hands-on exercises that will keep you hooked throughout the book.

BookJul 2020474 pages

Hands-On Ensemble Learning with Python

Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models.

BookJul 2019298 pages

Deep Learning with R Cookbook

This book will help you get through the problems that you face during the execution of different tasks and understand hacks in deep learning. With unique recipes, you will implement various deep learning architectures using R 3.5.x. You will cover complex algorithms to perform tasks such as reinforcement learning, GANs, advanced neural networks and more.

BookFeb 2020328 pages

TensorFlow Machine Learning Projects

This book will show you how to take advantage of TensorFlow’s most appealing features - simplicity, efficiency, and flexibility - in various scenarios. You will gain cutting-edge insights into using TensorFlow’s offerings for your problems and learn practical hacks to successfully implement real-world machine learning projects.

BookNov 2018322 pages

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

BookAug 2023230 pages5

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages4

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages1

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

BookAug 2023684 pages

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages5

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages2

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

BookOct 2023636 pages5

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

BookAug 2023420 pages5

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

BookDec 2023240 pages

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

BookNov 2023664 pages