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

Brett Lantz

€8.99 ~~€35.99~~

4.2 (46 Ratings)

eBook Apr 2019 458 pages 3rd Edition

Brett Lantz

€8.99 ~~€35.99~~

4.2 (46 Ratings)

eBook Apr 2019 458 pages 3rd Edition

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Key benefits

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

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.

Who is this book for?

Data scientists, students, and other practitioners who want a clear, accessible guide to 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

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Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

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DRM FREE - Read whenever, wherever and however you want

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Andrew Horn Jan 14, 2020

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.

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Andreas Dorta Jan 11, 2021

Most other books I know on this topic are far too theoretical for me.Brett provides a very practical approach.

Ariful Islam Mondal Apr 27, 2019

Excellent book. I have come across one of the best books so far on Machine Learning using R. The author has made sure that the book is readable for both for a new users of R programming as well as who are new to machine learning with multiple case studies. This is also a great book for people who want to refresh their memories in key techniques of machine learning. I am an experience data science professional and I would recommend this book to readers looking to understand how to build machine learning models using R. Thanks to author for writing this book.

floren25 Jun 28, 2020

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.

Ashish Tambadkar May 13, 2019

I am a complete beginner to R as well as Machine learning, this book has clearly stated every concept and made me understand machine learning even better. I highly recommend this book

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

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