Books explains basics of machine learnig in a way that quite easy to understand. does not go deep into maths involved, but sufficiently to allow understandinf of algorithms explained.
Very usefull especially if you plan to use weka datamining tool as pretty much everythin available in weka is explained in this book to degree that you choose suitable algorithms and tune them correctly
But it is so badly written I can't get through it! It doesn't flow well and it seems to jump into things with little or no context in many places.
At times I even had to refer to other books such as 'Speech and Language Processing: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition' by Jurafsky to get a proper explanation and work through of the theories.
I'll be looking for another book in this area - don't buy this one.
Having read the first edition, the authors earn the extra rating because they've managed to improve on their work and practical WEKA resource offering. Without a doubt, an essential read for people who are both new and experienced in the fields of data mining, descriptive & predictive analytics or state & behavioural modelling.
The volume of material on the market today is still quite limited and in the gap between the first and second edition of this book, quite a lot has actually changed in the field. In my view, book content has only marginally progressed with the times, perhaps in favour of attempting to attract and activate new members, practictioners and commercially oriented researchers to the fore of data mining. It's a bold step to evolve material as the field evolves; those breaking new ground in this area should be more visible and offered greater support.
I believe that there is room in the market now for some revised materials covering anomalised commercial implementations of Advanced Data Mining & AI Concepts. A small community of authors could plug this gap really well.