Programming Collective Intelligence

(Source Template)

    <?xml version="1.0"?>
    
    <page title="Programming Collective Intelligence" keywords="ai, filtering, bayesian, genetic algorithms">
    
    <item>
      <p>Author: Toby Segaran</p>
      <p>ISBN: <isbn>0-596-52932-5</isbn></p>
      <p>Publisher: O'Reilly Associates</p>
      <p>Reviewed by: Simon Wistow</p>
    </item>
    <item>
    <p>
    The field of data mining is a tricky one to write about. For a 
    start what you're mining depends on the nature of your business and the 
    shape of the data - there is no one-size-fits-all technique, no off the 
    shelf, drag and drop solution.
    </p>
    
    <p>
    Secondly some of the techniques require some pretty tricksy maths and 
    even if you do understand them then once they're applied you still have 
    to interpret the results and tweak the multitude of input variables. 
    Building a data mining tool - from a search engine to a collaborative 
    filter to a genetic algorithm - is an art as much as a science or 
    engineering problem.
    </p>
    
    <p> 
    So all that said, you should buy this book.
    </p>
    
    <p> 
    Reading it will help you understand why I just said all that. But it 
    will also give you a bunch more techniques in your mental toolbox so 
    that when you're looking at a problem you can think "Ooooh! I 
    remembering reading about some problem like that" and then you can go 
    pick up the book again and use it as a reference manual rather than 
    reading it from cover to cover.
    </p>
    
    <p> And there's a goodly number of techniques to pick up and there's a lot to 
    cover - there are chapters on collaborative filtering and recommendation systems, 
    clustering and group discovery, search and ranking techniques, document filtering, 
    Bayesian classification, kernel methods and support-vector machines, and genetic 
    algorithms, amongst others.
    </p>
    
    <p>
    Each chapter gives an overview of the problem domain, gives an example 
    problem and then walks the reader through a simple solution. The 
    problems with the solution are then highlighted and various enhancements 
    are shown. 
    </p>
    
    <p>
    The techniques are demonstrated in Python - although they are all clear, 
    understandable and perfectly legible to any competent programmer, 
    especially a scripting language programmer. Just enough detail is 
    covered to give you a solid grounding without getting you bogged down.
    </p>
    
    <p>
    In summary - this is well worth your 20 quid, even more so if you can 
    get your company to pay for it. If you're working with existing data 
    this may spark off an inspiration that will let you add some new 
    features or up your accuracy. Or if you're presented with a problem this 
    book may give you techniques that will help you solve it without having 
    to work everything out from first principles. It's well written manual 
    that'll handily expand your repetoire.
    </p> 
    
    </item>
    </page>
    
    

    <?xml version="1.0"?>
    
    <page title="Programming Collective Intelligence" keywords="ai, filtering, bayesian, genetic algorithms">
    
    <item>
      <p>Author: Toby Segaran</p>
      <p>ISBN: <isbn>0-596-52932-5</isbn></p>
      <p>Publisher: O'Reilly Associates</p>
      <p>Reviewed by: Simon Wistow</p>
    </item>
    <item>
    <p>
    The field of data mining is a tricky one to write about. For a 
    start what you're mining depends on the nature of your business and the 
    shape of the data - there is no one-size-fits-all technique, no off the 
    shelf, drag and drop solution.
    </p>
    
    <p>
    Secondly some of the techniques require some pretty tricksy maths and 
    even if you do understand them then once they're applied you still have 
    to interpret the results and tweak the multitude of input variables. 
    Building a data mining tool - from a search engine to a collaborative 
    filter to a genetic algorithm - is an art as much as a science or 
    engineering problem.
    </p>
    
    <p> 
    So all that said, you should buy this book.
    </p>
    
    <p> 
    Reading it will help you understand why I just said all that. But it 
    will also give you a bunch more techniques in your mental toolbox so 
    that when you're looking at a problem you can think "Ooooh! I 
    remembering reading about some problem like that" and then you can go 
    pick up the book again and use it as a reference manual rather than 
    reading it from cover to cover.
    </p>
    
    <p> And there's a goodly number of techniques to pick up and there's a lot to 
    cover - there are chapters on collaborative filtering and recommendation systems, 
    clustering and group discovery, search and ranking techniques, document filtering, 
    Bayesian classification, kernel methods and support-vector machines, and genetic 
    algorithms, amongst others.
    </p>
    
    <p>
    Each chapter gives an overview of the problem domain, gives an example 
    problem and then walks the reader through a simple solution. The 
    problems with the solution are then highlighted and various enhancements 
    are shown. 
    </p>
    
    <p>
    The techniques are demonstrated in Python - although they are all clear, 
    understandable and perfectly legible to any competent programmer, 
    especially a scripting language programmer. Just enough detail is 
    covered to give you a solid grounding without getting you bogged down.
    </p>
    
    <p>
    In summary - this is well worth your 20 quid, even more so if you can 
    get your company to pay for it. If you're working with existing data 
    this may spark off an inspiration that will let you add some new 
    features or up your accuracy. Or if you're presented with a problem this 
    book may give you techniques that will help you solve it without having 
    to work everything out from first principles. It's well written manual 
    that'll handily expand your repetoire.
    </p> 
    
    </item>
    </page>