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Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition by [Brett Lantz]

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Brett Lantz

Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition Kindle Edition

by Brett Lantz (Author) Format: Kindle Edition

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Solve real-world data problems with R and machine learning

Key Features

Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond
Harness the power of R to build flexible, effective, and transparent machine learning models
Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz

Book Description

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.

Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.

This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

What you will learn

Discover the origins of machine learning and how exactly a computer learns by example
Prepare your data for machine learning work with the R programming language
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 — the basis of deep learning
Avoid bias in machine learning models
Evaluate your models and improve their performance
Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

Who this book is for

Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Introducing Machine Learning
Managing and Understanding Data
Lazy Learning – Classification Using Nearest Neighbors
Probabilistic Learning – Classification Using Naive Bayes
Divide and Conquer – Classification Using Decision Trees and Rules
Forecasting Numeric Data – Regression Methods
Black Box Methods – Neural Networks and Support Vector Machines
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Groups of Data – Clustering with k-means
Evaluating Model Performance
Improving Model Performance
Specialized Machine Learning Topics

Length: 460 pages

Enhanced Typesetting: Enabled

Page Flip: Enabled

Due to its large file size, this book may take longer to download

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From the Publisher

Machine Learning with R: Expert techniques for predictive modeling, Third Edition

Turn big data into actionable intelligence!

Make sense of data with R, this book, and the powerful machine learning tools contained within. Discover how machine learning works and how you can use it to solve real-world problems.

For example:

Train models to tell the difference between safe and poisonous mushrooms
Review breast cancer screening results to detect samples with cancerous cells
Analyze data from Space Shuttle Challenger’s stress tests to discover the fault that destroyed it
Help health insurers identify the riskiest customer segments
Mix the right formula for concrete for the weight and stress it will bear.

Objective and approach

This book takes a data science approach to machine learning. You will apply key machine learning approaches to a variety of data sets. It teaches the necessary practice and theory to make you a successful machine learning practitioner. Brett Lantz explains every concept and code sample clearly and simply. Each chapter covers a key machine learning concept. The book acts as both a friendly tutorial and a reference guide.

Techniques

Machine Learning with R - Third Edition covers all of the key machine learning techniques, such as:

Nearest neighbor classification
Bayesian methods
Decision trees, rules, and support vector machines
Regression
Neural networks

It explains how to prepare data for machine learning, evaluate and improve your models, and avoid unhelpful bias.

About the Author

Brett Lantz

Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

--This text refers to the paperback edition.

Product details

ASIN : B07PYXX3H5
Publisher : Packt Publishing; 3rd edition (April 15, 2019)
Publication date : April 15, 2019
Language : English
File size : 38477 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 460 pages
Lending : Not Enabled

Best Sellers Rank: #68,315 in Kindle Store (See Top 100 in Kindle Store)
- #5 in Machine Theory (Kindle Store)
- #9 in Data Mining (Kindle Store)
- #12 in Algorithm Programming

Customer Reviews:
135 ratings

Customer reviews

5 star		75%
4 star		16%
3 star		4%
2 star		2%
1 star		3%

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Top reviews from the United States

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Albategnius

Resources (were) missing - now restored!

Reviewed in the United States on August 7, 2019

Verified Purchase

I received the book just recently. I will update my review of the contents later. Two things I noticed right away are (a) The 'dataspelunking.com' website mentioned above (in the Amazon description) and in the book, is an empty shell; and (b) The link in the preface pointing to a page containing a PDF of color-images of the screenshots/diagrams in the book is broken, leading to a 404 message. The kindest thing one can say about this is that the book has been rushed to publication and released before the associated resources are ready. At worst this is a misrepresentation. While those color figures are not a big loss, this carelessness does not bode well for the attention to detail with which the book was produced, especially since it is in its 3rd edition.

Edited: Packt repaired the broken link and contacted me here to point that out. Very nice! I am revising my rating. I will revise again for content after I finish the book!

7 people found this helpful

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Kelsey Huntzberry

A good basic beginner’s book

Reviewed in the United States on August 28, 2019

Verified Purchase

UPDATE: I am increasing the number of stars on this review. I do not take back what I said, this book is quite basic. However, if you are looking for a tutorial with very basic concepts of machine learning, this is a good beginner’s book. You will definitely need to supplement this with something that has more detail. It’s deep learning description is not useful, but for basic machine learning this is a good place to start.

This book was disappointing. I can understand trying to simplify material so a mass audience can understand it, but the author went too far here. There isn’t nearly enough content to get even a basic grasp of the main machine learning tools. There are also huge holes in the content. The book is based almost solely on classification models and does not delve into numeric prediction models hardly at all, apart from linear regression. Given the price, I’m really disappointed. It’s going to stay on my shelf collecting dust.

9 people found this helpful

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Andrew Horn

Best Machine Learning Book out there!! WOW

Reviewed in the United States on January 14, 2020

Verified Purchase

This is an outstanding book and by far the best machine learning book of the several I have read. I highly recommend.

1) The author writes this in a way that you can perform machine learning tasks in R without any prior experience using R or ML. He gets you up to speed quickly on how to download R Studio and explains everything you need to know about R in order to perform the ML tasks that are described later in the book. He doesn't explain all of the things you can do in R as in many other books, because you don't necessarily need to know everything about R to perform ML tasks and this book is about ML. So this is obviously not a book that is exhaustive of everything possible in R.

2) The structure of this book is outstanding and very logical. Within each of the chapters on Machine Learning models, he describes the model and how it works, includes a section covering the model's syntax in R, the strengths and weaknesses of each model, compares the model to the prior models discussed, and then uses that foundation to walk you through an example of applying each model to real data. Within the examples, he uses the 5 steps of a modeling process: 1) collecting data, 2) exploring and prepping the data, 3) training the model, 4) evaluating model performance, and 5) Improving model performance. I love this structure!

3) His explanations of everything are outstanding. I've read several ML books and he by far is the best at explaining things in a way that people can clearly understand. If the model involves statistical concepts, he explains what you need to know very well.

4) The book covers many ML models: K-Nearest Neighbors, Naive Bayes, Decision Trees, Classification Rules, Regression, Regression Trees, Model Trees, Neural Networks, Support Vector Machines, Association Rules, K-Means Clustering, and Random Forests. For an advanced Data Scientist, this may not seem like an exhaustive list. However, I feel that for a beginning data scientist or someone that is just interested in getting a good overview of data modeling, I feel this is a great choice of model coverage.

Bottom line- if you're at all interested in machine learning in R, this is the book to get. If you're already an advanced data scientist with years of experience, this may not be the best text for you, though.

5 people found this helpful

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Bom V.

A Fast Way to Get Up and Running with ML

Reviewed in the United States on November 12, 2019

Verified Purchase

I'd have to say that this book is a simple and clear concise introduction to both R and ML. Probably one of the best books on ML with R I've used. It starts of with a robust introduction to R and stats that'll give you the tools to play around with R. Next, the book goes into the ML with good illustrations using physical representations for easier understanding. It goes on to some vector/component concepts in SVM (obviously) but still retains the intuitive properties of the book. The book chapters, generally, are independent and can be read as stand alone without much loss in understanding. Overall, a great book to give you the confidence to move forward in ML.

2 people found this helpful

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Amazon Customer

Really good

Reviewed in the United States on August 18, 2019

Verified Purchase

Really like how it goes through all of the major models and is easy to read. Learning data science and it's SO HARD to find books that are written for people who are new to the field/not well-versed in all the technical. Loved that this one was so easy to get through!

3 people found this helpful

Helpful

Huijeong Jeong

Soso

Reviewed in the United States on March 17, 2020

Verified Purchase

Easy but i guess this is for programming beginners. I already learned some languages and R programming before so it doesn't help me that much.

One person found this helpful

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Jim Burke

Helps everyone with R

Reviewed in the United States on November 17, 2020

Verified Purchase

Another good R book. In fact I can recommend all three books

Helpful

Sriram Dandapani

3rd edition is a carbon copy of 2nd edition

Reviewed in the United States on May 15, 2020

Verified Purchase

The original book is great. But for a user who is looking for updates 4 years since the previous edition, this book falls flat. It is almost identical to the previous edition which implies newer techniques are not incorporated (which may be ok depending on the landscape of the subject domain, but the book title was misleading)

Helpful

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Max Latey

Everything you need in one place to get started

Reviewed in the United Kingdom on January 14, 2020

Verified Purchase

Superb! Everything you need in one book to get started. Concentrates on old school machine learning more than deep n/n but there are other books for that.

floren25

Un libro inesperadamente entretenido

Reviewed in Spain on June 28, 2020

Verified Purchase

Puede resultar chocante, como poco, ver que alguien califica de «entretenido» un libro más bien técnico como éste, pero resulta que el calificativo es bastante apropiado. Lo es porque Brett lantz ha escrito una obra muy bien estructurada y pedagógicamente escrita sobre un tema que a muchos puede resultar intimidatorio, pero que él se las ingenia para volver atractivo.
El libro es una introducción amable y facilitada a catorce algoritmos de «Machine Learning» (expresión que suele traducirse al castellano como «aprendizaje automático»), una muestra representativa, aunque desde luego no exhaustiva, de los métodos de Machine Learning en circulación. Los catorce algoritmos cubren desde k-nearest neighbors hasta Random Forests, pasando por Decision trees y Association rules. Los distintos algoritmos (de aprendizaje supervisado, no supervisado y de meta-aprendizaje) cubren cuatro tipos de tareas: clasificar, predecir, detectar patrones y agrupar.
Aunque ya tenía noticia de ello, me parece digno de ser resaltado que los algoritmos más potentes, como Artificial Neural Networks o Support Vector Machines, son lo que Lantz llama «algoritmos de caja negra»: sabemos que funcionan bien pero no sabemos por qué funcionan bien.
Uno de los problemas frecuentes con este tipo de libros es que hay complementos que tienes que bajar de Internet y que no siempre se comportan como deberían. En este caso, ¡loado sea el cielo!, no es así: las bases de datos y el código escrito en el lenguaje R funcionan sin problemas. Sólo en los tres últimos capítulos me he llevado algún tropezón. Especialmente en el último, que es con diferencia el de contenido más avanzado: computación en la nube, big data y demás.
Pero, a pesar de lo que acabo de decir, se aprende mucho con este texto, dado el esmero que pone el autor en explicar todos los detalles (como las líneas de código que pudieran resultar más intrigantes) y en no dejar a nadie atrás.
En suma, una excelente iniciación a Machine Learning usando el software R, el más empleado por los estadísticos, aunque no el más común en aprendizaje automático.

3 people found this helpful

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Robert Langdon

too basic

Reviewed in Italy on October 12, 2020

Verified Purchase

too basic and too afraid to dive into the mathematical and statistical details of the tecniques proposed, and probably cause the target is all those who go like "omg machine learning so cool maann".
If you don't know statistics, there is no machine learning for you people

3 people found this helpful

Guil

Libro magnífico. Reparto lamentable (el repartido me REVENTÓ EL BUZÓN)

Reviewed in Spain on July 28, 2020

Verified Purchase

El libro es un clásico para el aprendizaje de Machine Learning (procesos de regresión, Random Forests, SVM, etc.). Es fundamental para cualquier persona con un nivel medio de programación en R.

La principal queja reside en el reparto. El libro llegó con antelación y al no haber nadie en casa el repartidor de Amazon reventó el buzón para intentar meter el paquete, que a su vez terminó reventado (afortunadamente el libro ha sufrido daños mínimos). Al final he tenido que recoger el libro de un vecino y perder una mañana intentando reparar el buzón y buscando las partes que faltan.

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Josh

Amazing!

Reviewed in Canada on November 17, 2020

Verified Purchase

If you want to use machine learning to solve problems, or for your job, this book is amazing. It explains the math/statistics involved in the algorithms, and it gives a crash course in R language (though I was already familiar with R before reading this book). This book does an excellent job in explaining how to practically use machine learning to solve real-life problems. I am very happy that I read this book.

One person found this helpful