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Get to grips with the tidyverse, challenging data, and big data

Create clear and concise data and model visualizations that effectively communicate results to stakeholders

Solve a variety of problems using regression, ensemble methods, clustering, deep learning, probabilistic models, and more

Description

Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic. Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering. With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights. Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques. Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.

Who is this book for?

This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.

What you will learn

Learn the end-to-end process of machine learning from raw data to implementation

Classify important outcomes using nearest neighbor and Bayesian methods

Predict future events using decision trees, rules, and support vector machines

Forecast numeric data and estimate financial values using regression methods

Model complex processes with artificial neural networks

Prepare, transform, and clean data using the tidyverse

Evaluate your models and improve their performance

Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow

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Frequently bought together

The Statistics and Machine Learning with R Workshop

$49.99

Interpretable Machine Learning with Python

$49.99

$49.99

Total $ 149.97

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Ham Sandwich May 29, 2023

This book serves several functions (in my opinion). The first is as a language-neutral guide to the most common data science/machine learning approaches; the second is as a fully-developed guide for how to use R (and really RStudio).The level of writing is excellent for the target audience - not necessarily a full-fledged developer, but a scientist or analyst who needs a platform for doing the work. Proponents of python will point to speed issues and the fact that python is in fact a foundational language - something you can find lurking in your OS - but that's not the point. R is easy to use, doesn't suffer from myriad package and environment woes, and produces great visuals.Back to the book: the standard list of ML topics is covered, with just enough but not too much math and working code. Best yet, the writer illustrates that everything that you can do in python for data science can be done in R (why should only python users suffer the indignities of TensorFlow?) and probably should be if productivity is your key objective.R's other superpower is the Hadley Wickham factor: he is responsible for making R's so-called tidyverse a part of your universe. This means that the cleaning, preparation, representation, and all that good stuff has been intelligently implemented (there are not many different ways of doing the slightly wrong thing as in python) to protect the user.I highly recommend R in general and this book in particular for getting started or updating one's ML and/or R background.

Amazon Verified review

Placeholder Jun 14, 2023

Machine Learning with R" by Packt Publishing is an excellent resource for anyone looking to explore machine learning using the R programming language. With its practical approach, comprehensive coverage of algorithms, and emphasis on the R ecosystem, the book equips readers with the necessary tools and knowledge to tackle real-world machine learning problems. Whether you are a student, data scientist, or R enthusiast, this book serves as a valuable companion on your journey to mastering machine learning with R.

Brian Julius, Power BI Expert and Instructor Jun 08, 2023

This new edition represents a *major* revision, with five completely new chapters, encompassing 300 additional pages.Here are the top five reasons why I find this book so valuable:🔸Unique FocusUnlike many other books on this topic which are geared towards those with extensive math/CS/ #datascience backgrounds, this book is aimed at the business user and assumes no prior knowledge of ML learning, statistics, or R.The author (Brett Lantz, Sr. Data Scientist at Playstation) spends the first two chapters of the book providing a very clear, concise introduction to the field of ML, followed by a primer on the key R concepts and techniques that will be used throughout the book.🔸Covers "The Hits"ML is a huge field, so covering it comprehensively in one book is impossible. However, at well over 700pp, this book addresses the major, most commonly used models including:🔹 Nearest Neighbor (k-nn)🔹 Naive Bayes🔹 Decision Trees (C5.0, 1R and RIPPER algorithms)🔹 Regression Models (OLS, multiple linear, generalized linear, logistic regression and regression trees)🔹 Neural Networks🔹 Support Vector Machines🔹 Association Rules🔹 K-Means Clustering🔸Highly PracticalComplementing Brett's plain-language style is his choice of detailed, practical examples to illustrate exactly how to implement each model via best practices.His examples span diverse domains, including diagnosing breast cancer, filtering spam, identifying bank loans most likely to fail, identifying poisonous mushrooms, predicting insurance claims, forecasting customer churn, performing OCR, identifying products most likely to be purchased together, and determining market segments.🔸Provides a Clear Path for Additional LearningWhile the focus of this book is not a deep dive into the advanced mathematics and complex variants of each model type, throughout the text there are callouts to other resources that the author recommends for further study.🔸More Flexible Than the Title Would IndicateSome Python users might look at the title of this book and dismiss it as "an R book". That would be a huge mistake, since the vast majority of it is dedicated to non-language-specific explanations of ML models and how to implement them.Particularly in this era of ChatGPT, it would be a simple matter to convert the R code in this book to Python.Overall, I very highly recommend this book to anyone looking to develop a solid fundamental understanding of the theory and practice associated with common ML models.

DudeGuy Jun 08, 2023

Highly recommend this work. Principles and code are clearly explained a broken down such that newer and seasoned experts can benefit from this edition. I own the 3rd Edition and was so pleased with it that I found it worth reinvesting in this updated and expanded 4th Edition which includes perhaps 70% more content.

Ariful Islam Mondal Mar 05, 2024

This book is very comprehensive and easy to follow. I would recommend this book to any one learning both R and Machine Learning for the first time as well as a good refresher for practitioners like me.This book comes with 700+ pages detail theory, pictorial explanations and practical examples.

Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data , Fourth Edition

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