Machine Learning with R

First off, I am newbie to both machine learning and R and wanted find a starting point somewhere. I browsed around many books before deciding on this one. The writing style of Mr. Lantz is provided in a very understandable/readable manner. It's akin to someone sitting next to you and explaining things in a down to earth, layman's fashion rather than try to "tech speak" you to death with complicated explanations (aka formal textbook). Just the right amount of hand holding for me. I highlight quite bit and it's actually difficult with this book as there isn't much fluff. He's very succinct. The books states that it's for someone who know some ML and no R or R and no ML. I don't know either and the material is digestible except for one thing: review your stats! I took statistics long ago in college and never really learned it well the first time so I had stop and reread core concepts before continuing. Do yourself a favor and review basic statistics and probability before you start this book. I read both "Naked Statistics" and "Statistics in Plain English" and it helped me a great deal (and probably will continue to do so since it appears a bulk of machine learning is stats and prob). Currently into about a third of the way in and I am finding it to be very enjoyable and practical. Other reviewers point out that this book is too basic and this may be the case, but for someone like me who is starting from absolute scratch and who needs to understand basic ML concepts (AND basic R) I find it a great book. Will post an addendum once I complete it.

This is a great book. I liked the way authors highlight syntax for models and discuss strengths and weeknesses. It has a nice balance of theory and hands-on training. However, I would need to use a R book, such as R in action, in conjunction with it.I have looked at many books on the topic. I will put my review for all of these. Perhaps this can save you some time.1) http://www.amazon.com/dp/0470650931 : Good theoretical book, but badly written and does not have any hands on exercise.2) http://www.amazon.com/dp/1466503963 : This is another great book. Good balance of theory and hands-on exercise. This is an excellent book to start learning data mining and R. However, this book relies on a GUI RCommander. It does a good job and one can do a lot with it but it has its limitations. However, I will still use this book.3) http://www.amazon.com/dp/1439810184 : This is an advanced book and heavily entrinched in cases. This makes it difficult to replicate things unless your work is directly related to one of the case studies covered.4) http://www.amazon.com/dp/0133412938 : Good examples, but does not explain much about the interpretation. This leaves one wondering what is the purpose of certain graph, what are the axis and how to interpret it. if appropriate explanation is added, this would be an excellent book.5) http://www.amazon.com/dp/111844714X : This book is very expensive and almost totally devoid of any theory or discussion. I would not use it.6) http://www.amazon.com/dp/1441998896: This is a decent book. It relies on another GUI, Rattle. It is a strong contender to the book 2 in this list.

This book provides a quick overview of some of the most popular machine learning algorithms and their implementation in R. I found it easy to read and following the examples was fun. However, the edition is not the best, as you'll find plenty of typos.If you are starting with machine learning (or thinking about it), this could be a useful reference to see how it can be used (there are plenty of practical examples) and get a basic notion of how each algorithm is implemented. If you already know a lot about it, this book is not for you! The price could be a bit lower and the explanations are too simple compared to other resources available online.Overall, I gave this book 4 stars because of the practical examples, which are a useful way to see how machine learning can be used.PS: Personally, I think this book would be a good companion to the MIT "Analytics Edge" MOOC. Some of the methods overlap between the two, and it follows the same format of learning by doing.

I am very happy that I bought this book. The book covers a good introduction of mechine learning concepts both theory (introduction level and easy to understand) and implementations of these concepts in R. These topics include:(0) Basic ideas and concepts of machine learning (data collection, preparation, training, tuning, and so on)(1) KNN classification with example in diagnosing breast cancer(2) Naive Bayes classification with example in spam classification for sms message(3) Decision Tree classification with example in identifying risky bank loans(4) Basic Regression Method (Forecasting Numeric Data) with example in predicting medical expense using linear regression(5) Regression Trees and Model Trees with example in estimating the quality of wines(6) Neural Network with example in estimating the strength of concrete(7) Support Vector Machines with example in Optical Character Recognition(8) Association Rules with example in Market Basket Analysis(9) Clustering with k-means(10) Methods of measuring performance of machine learning algorithms(11) Methods to improve performance of machine learning algorithmsIn summary, this is a very good book in my opinion and I would recommend this book to anyone who want to study machine learning algorithm,