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4 people found this helpful

ByD A. Springateon 31 January 2014

[Full disclosure - I was given a free review copy of the book from the publisher. This review refers to the ebook version]

This is the most recent of a group of books that try to explore machine learning from a programming, rather than purely mathematical, perspective. The book is highly successful in this respect and deserves a place on the bookshelf of any data scientist, Kaggler or statistician.

The book takes a slightly different tack from previous ones in this field (See 'Programming Collective Intelligence' and 'Machine learning for Hackers') in that it concentrates largely on the packages themselves and how to use them to solve real world ML problems, rather than focusing on coding up simple algorithms from scratch and running these on toy datasets. Perhaps this way the book doesn't provide as much insight into how the algorithm design, but it does make the book much more practically useful, particularly since it spends a good chunk of each chapter explaining the algorithm in simple, plain English.

The book is well laid out and written. Despite a slightly shaky start (do we really still think of ML in terms of Skynet, the Matrix and Hal?), the introduction is excellent and gives a pleasing summary of the philosophical and ethical issues surrounding machine learning and big data. Next, there is a thoughtful introduction to data management and exploratory data analysis that highlights important and often missed tips on things like getting data out of SQL databases. It introduces some basic R functions and concepts (some I had managed to miss up until now) without feeling like a tacked on 'R for beginners' chapter.

In the guts of the book, each chapter focuses on a group of related algorithms (KNN, Naive Bayes, Decision trees, Regression, Neural nets and SVMs, association rules, clustering) and has a good introduction to the algorithm in question, followed by sections on finding and cleaning data, implementing the algorithm on the data and evaluating and improving model performance. There are clear and easy to understand tables and descriptions of the important distinctions between the algorithms and the reasons for choosing one over another. The datasets the author has chosen are large and interesting enough to well illustrate the points being made without being frustratingly unwieldy and many of them are 'classic' machine learning datasets from places such as the UCI Machine Learning Data Repository.

Next, the book looks more deeply at evaluating and improving model performance and discusses important ensemble and meta-learning techniques like bagging, boosting and RandomForests. This section will be of particular interest to people wanting to enter Kaggle or other data science competitions because they show how to milk as much performance as possible from the basic algorithms described earlier in the book.

The final section discusses getting the algorithms to run on big datasets and improving the performance of R itself using tricks like the data.table and ff packages and parallel processing. This is the only section of the book that feels slightly rushed and many of these topics are discussed only briefly before linking to the relevant package documentation. This is only small criticism though, since coding up these kinds of systems will depend strongly on the data you have and these are difficult subjects to cover whilst retaining generality.

Obviously, the book cannot cover everything. It is decidedly light on graphs and has almost nothing on visualisation techniques and packages like ggplot2 which have become almost mandatory for doing data science today. Also, if you are new to R, you really want to get one of the excellent introductory books first and if you are new to ML, you probably want to spend a while learning some basic stats as well. Finally, this book doesn't pretend to be a deep text about the mathematics of the algorithms it covers. For that you will need to go for something like Bishop's classic 'Pattern Recognition and Machine Learning' and be prepared to put in some serious effort!

In short, if you are looking for a practical guide to implementing ML algorithms on real data and if you are more comfortable thinking in R code than in mathematical equations, this is the book for you and is probably the best that I have seen on the subject so far.

This is the most recent of a group of books that try to explore machine learning from a programming, rather than purely mathematical, perspective. The book is highly successful in this respect and deserves a place on the bookshelf of any data scientist, Kaggler or statistician.

The book takes a slightly different tack from previous ones in this field (See 'Programming Collective Intelligence' and 'Machine learning for Hackers') in that it concentrates largely on the packages themselves and how to use them to solve real world ML problems, rather than focusing on coding up simple algorithms from scratch and running these on toy datasets. Perhaps this way the book doesn't provide as much insight into how the algorithm design, but it does make the book much more practically useful, particularly since it spends a good chunk of each chapter explaining the algorithm in simple, plain English.

The book is well laid out and written. Despite a slightly shaky start (do we really still think of ML in terms of Skynet, the Matrix and Hal?), the introduction is excellent and gives a pleasing summary of the philosophical and ethical issues surrounding machine learning and big data. Next, there is a thoughtful introduction to data management and exploratory data analysis that highlights important and often missed tips on things like getting data out of SQL databases. It introduces some basic R functions and concepts (some I had managed to miss up until now) without feeling like a tacked on 'R for beginners' chapter.

In the guts of the book, each chapter focuses on a group of related algorithms (KNN, Naive Bayes, Decision trees, Regression, Neural nets and SVMs, association rules, clustering) and has a good introduction to the algorithm in question, followed by sections on finding and cleaning data, implementing the algorithm on the data and evaluating and improving model performance. There are clear and easy to understand tables and descriptions of the important distinctions between the algorithms and the reasons for choosing one over another. The datasets the author has chosen are large and interesting enough to well illustrate the points being made without being frustratingly unwieldy and many of them are 'classic' machine learning datasets from places such as the UCI Machine Learning Data Repository.

Next, the book looks more deeply at evaluating and improving model performance and discusses important ensemble and meta-learning techniques like bagging, boosting and RandomForests. This section will be of particular interest to people wanting to enter Kaggle or other data science competitions because they show how to milk as much performance as possible from the basic algorithms described earlier in the book.

The final section discusses getting the algorithms to run on big datasets and improving the performance of R itself using tricks like the data.table and ff packages and parallel processing. This is the only section of the book that feels slightly rushed and many of these topics are discussed only briefly before linking to the relevant package documentation. This is only small criticism though, since coding up these kinds of systems will depend strongly on the data you have and these are difficult subjects to cover whilst retaining generality.

Obviously, the book cannot cover everything. It is decidedly light on graphs and has almost nothing on visualisation techniques and packages like ggplot2 which have become almost mandatory for doing data science today. Also, if you are new to R, you really want to get one of the excellent introductory books first and if you are new to ML, you probably want to spend a while learning some basic stats as well. Finally, this book doesn't pretend to be a deep text about the mathematics of the algorithms it covers. For that you will need to go for something like Bishop's classic 'Pattern Recognition and Machine Learning' and be prepared to put in some serious effort!

In short, if you are looking for a practical guide to implementing ML algorithms on real data and if you are more comfortable thinking in R code than in mathematical equations, this is the book for you and is probably the best that I have seen on the subject so far.

One person found this helpful

ByLaurent Franckxon 9 May 2014

The book is a (very) gentle introduction to machine leanring with R, assuming no prior knowledge of either subject. It gives a good overview of possible techniques, with extensive examples. However more advanced users will not find rigorous mathematical or statistical material.

ByD A. Springateon 31 January 2014

[Full disclosure - I was given a free review copy of the book from the publisher. This review refers to the ebook version]

This is the most recent of a group of books that try to explore machine learning from a programming, rather than purely mathematical, perspective. The book is highly successful in this respect and deserves a place on the bookshelf of any data scientist, Kaggler or statistician.

The book takes a slightly different tack from previous ones in this field (See 'Programming Collective Intelligence' and 'Machine learning for Hackers') in that it concentrates largely on the packages themselves and how to use them to solve real world ML problems, rather than focusing on coding up simple algorithms from scratch and running these on toy datasets. Perhaps this way the book doesn't provide as much insight into how the algorithm design, but it does make the book much more practically useful, particularly since it spends a good chunk of each chapter explaining the algorithm in simple, plain English.

The book is well laid out and written. Despite a slightly shaky start (do we really still think of ML in terms of Skynet, the Matrix and Hal?), the introduction is excellent and gives a pleasing summary of the philosophical and ethical issues surrounding machine learning and big data. Next, there is a thoughtful introduction to data management and exploratory data analysis that highlights important and often missed tips on things like getting data out of SQL databases. It introduces some basic R functions and concepts (some I had managed to miss up until now) without feeling like a tacked on 'R for beginners' chapter.

In the guts of the book, each chapter focuses on a group of related algorithms (KNN, Naive Bayes, Decision trees, Regression, Neural nets and SVMs, association rules, clustering) and has a good introduction to the algorithm in question, followed by sections on finding and cleaning data, implementing the algorithm on the data and evaluating and improving model performance. There are clear and easy to understand tables and descriptions of the important distinctions between the algorithms and the reasons for choosing one over another. The datasets the author has chosen are large and interesting enough to well illustrate the points being made without being frustratingly unwieldy and many of them are 'classic' machine learning datasets from places such as the UCI Machine Learning Data Repository.

Next, the book looks more deeply at evaluating and improving model performance and discusses important ensemble and meta-learning techniques like bagging, boosting and RandomForests. This section will be of particular interest to people wanting to enter Kaggle or other data science competitions because they show how to milk as much performance as possible from the basic algorithms described earlier in the book.

The final section discusses getting the algorithms to run on big datasets and improving the performance of R itself using tricks like the data.table and ff packages and parallel processing. This is the only section of the book that feels slightly rushed and many of these topics are discussed only briefly before linking to the relevant package documentation. This is only small criticism though, since coding up these kinds of systems will depend strongly on the data you have and these are difficult subjects to cover whilst retaining generality.

Obviously, the book cannot cover everything. It is decidedly light on graphs and has almost nothing on visualisation techniques and packages like ggplot2 which have become almost mandatory for doing data science today. Also, if you are new to R, you really want to get one of the excellent introductory books first and if you are new to ML, you probably want to spend a while learning some basic stats as well. Finally, this book doesn't pretend to be a deep text about the mathematics of the algorithms it covers. For that you will need to go for something like Bishop's classic 'Pattern Recognition and Machine Learning' and be prepared to put in some serious effort!

In short, if you are looking for a practical guide to implementing ML algorithms on real data and if you are more comfortable thinking in R code than in mathematical equations, this is the book for you and is probably the best that I have seen on the subject so far.

This is the most recent of a group of books that try to explore machine learning from a programming, rather than purely mathematical, perspective. The book is highly successful in this respect and deserves a place on the bookshelf of any data scientist, Kaggler or statistician.

The book takes a slightly different tack from previous ones in this field (See 'Programming Collective Intelligence' and 'Machine learning for Hackers') in that it concentrates largely on the packages themselves and how to use them to solve real world ML problems, rather than focusing on coding up simple algorithms from scratch and running these on toy datasets. Perhaps this way the book doesn't provide as much insight into how the algorithm design, but it does make the book much more practically useful, particularly since it spends a good chunk of each chapter explaining the algorithm in simple, plain English.

The book is well laid out and written. Despite a slightly shaky start (do we really still think of ML in terms of Skynet, the Matrix and Hal?), the introduction is excellent and gives a pleasing summary of the philosophical and ethical issues surrounding machine learning and big data. Next, there is a thoughtful introduction to data management and exploratory data analysis that highlights important and often missed tips on things like getting data out of SQL databases. It introduces some basic R functions and concepts (some I had managed to miss up until now) without feeling like a tacked on 'R for beginners' chapter.

In the guts of the book, each chapter focuses on a group of related algorithms (KNN, Naive Bayes, Decision trees, Regression, Neural nets and SVMs, association rules, clustering) and has a good introduction to the algorithm in question, followed by sections on finding and cleaning data, implementing the algorithm on the data and evaluating and improving model performance. There are clear and easy to understand tables and descriptions of the important distinctions between the algorithms and the reasons for choosing one over another. The datasets the author has chosen are large and interesting enough to well illustrate the points being made without being frustratingly unwieldy and many of them are 'classic' machine learning datasets from places such as the UCI Machine Learning Data Repository.

Next, the book looks more deeply at evaluating and improving model performance and discusses important ensemble and meta-learning techniques like bagging, boosting and RandomForests. This section will be of particular interest to people wanting to enter Kaggle or other data science competitions because they show how to milk as much performance as possible from the basic algorithms described earlier in the book.

The final section discusses getting the algorithms to run on big datasets and improving the performance of R itself using tricks like the data.table and ff packages and parallel processing. This is the only section of the book that feels slightly rushed and many of these topics are discussed only briefly before linking to the relevant package documentation. This is only small criticism though, since coding up these kinds of systems will depend strongly on the data you have and these are difficult subjects to cover whilst retaining generality.

Obviously, the book cannot cover everything. It is decidedly light on graphs and has almost nothing on visualisation techniques and packages like ggplot2 which have become almost mandatory for doing data science today. Also, if you are new to R, you really want to get one of the excellent introductory books first and if you are new to ML, you probably want to spend a while learning some basic stats as well. Finally, this book doesn't pretend to be a deep text about the mathematics of the algorithms it covers. For that you will need to go for something like Bishop's classic 'Pattern Recognition and Machine Learning' and be prepared to put in some serious effort!

In short, if you are looking for a practical guide to implementing ML algorithms on real data and if you are more comfortable thinking in R code than in mathematical equations, this is the book for you and is probably the best that I have seen on the subject so far.

ByTaimuron 11 December 2014

I found this book truly invaluable. It explained to me in detail all of the necessary basics and quirks of R. The software was brought to life in a way that elearning courses failed to achieve. The writing style is incisive and has a laser-focus on the likely questions to arise

In the mind of the practitioner. Highly recommended!

In the mind of the practitioner. Highly recommended!

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ByLaurent Franckxon 9 May 2014

The book is a (very) gentle introduction to machine leanring with R, assuming no prior knowledge of either subject. It gives a good overview of possible techniques, with extensive examples. However more advanced users will not find rigorous mathematical or statistical material.

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ByEYAD37on 9 July 2014

Very good practical guide. Previous knowledge in R and basics of machine learning will simplify reading and understanding, overall. It is a good read.

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ByAmazon Customeron 19 March 2015

Depending on the reader, a useful intermediate introduction. Not too technical for those readers not looking for this property.

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ByAmazon Customeron 19 November 2015

A useful, gentle introduction including code snippets to get you started.

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ByJoseph J Watkinson 23 June 2015

Really helpful and simple to understand.

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