Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: * Collaborative filtering techniques that enable online retailers to recommend products or media * Methods of clustering to detect groups of similar items in a large dataset * Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm * Optimization algorithms that search millions of possible solutions to a problem and choose the best one * Bayesian filtering, used in spam filters for classifying documents based on word types and other features * Using decision trees not only to make predictions, but to model the way decisions are made * Predicting numerical values rather than classifications to build price models * Support vector machines to match people in online dating sites * Non-negative matrix factorization to find the independent features in a dataset * Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect
Toby Segaran works as a Data Magnate at Metaweb Technologies. Prior to working at Metaweb, he started a biotech software company called Incellico which was later acquired by Genstruct. His book, "Programming Collective Intelligence" has been the best-selling AI book on Amazon for several months. He is the recipient of a National Interest Waiver for "People of Exceptional Ability", and currently lives in San Francisco. His blog and other information are located at kiwitobes.com.
这部书写的非常好,如果与机器学习课程结合起来看的话会起到事半功倍的效果。此书重于实践,从源代码中也能看懂各章的知识,可以说,读了此书,会对人工智能有个更深入的认识。
评分记得第一次读这本书的时候,是刚毕业在第一家小公司工作,虽然当时只是做Web,但是作为十人团队中少有的还有那么点数学基础的人,无可避免地把一些简单的非工程化的东西接了过来。当时有一个小任务是来做喜欢xxx的人也喜欢xxx,老大就把这本集体智慧编程扔给了我,说看这本书,...
评分通读全书了解了一下各个算法在实际生活中的应用,但是并没有跟着敲代码。一是API过于陈旧,很多都失效了;第二是完全没有数据公式的存在,是亮点,也是缺点。 有些代码完全不知道为什么是那样,只得 CRTL + C 和 CRTL + V 看下运行效果。 总体来说,能够给我们将算法应用于实际...
评分为了更好地学习本书,我从学习python开始到后来调试书中的网站实例。花了不少功夫,希望朋友们不要走弯路。这里提供了图文并茂的指导过程。请参考: http://blog.csdn.net/zjmwqx/article/details/7007438
评分这部书写的非常好,如果与机器学习课程结合起来看的话会起到事半功倍的效果。此书重于实践,从源代码中也能看懂各章的知识,可以说,读了此书,会对人工智能有个更深入的认识。
每章都是实例,实用性很强,基本的机器学习的方法都有涉及(regression涉及较少),只是代码一点儿没有pandas, sklearn, scipy, nltk等包,numpy也只是用了一下而已,不免有些过时,所以从实用性而言又打了一些折扣,但对于理解算法的原理却比直接用package要好许多。
评分豆瓣的由来。。
评分觉得应该给三星半。结构内容是不错,只是API各种过期,例如geocoding的那个。书上代码有问题的地方也不少。
评分优化Optimization和遗传算法两章讲的很生动。这本书的代码风格是教程式的,代码被拆分成了很多小段,每一小段都可以直接运行,方便你理解算法思想和自己写代码。最后的算法总结也十分实用,方便查找。这个书适合作为入门书,让你了解大量应用、Python API、算法,培养兴趣又开阔视野。美中不足是本书没有给扩展阅读,如果有的话就给他五星。
评分在图书馆读过一点,没有深入学习,获益匪浅.准备再读.
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