本书以机器学习与计算统计为主题背景，专门讲述如何挖掘和分析Web上的数据和资源，如何分析用户体验、市场营销、个人品味等诸多信息，并得出有用的结论，通过复杂的算法来从Web网站获取、收集并分析用户的数据和反馈信息，以便创造新的用户价值和商业价值。全书内容翔实，包括协作过滤技术（实现关联产品推荐功能）、集群数据分析（在大规模数据集中发掘相似的数据子集）、搜索引擎核心技术（爬虫、索引、查询引擎、PageRank算法等）、搜索海量信息并进行分析统计得出结论的优化算法、贝叶斯过滤技术（垃圾邮件过滤、文本过滤）、用决策树技术实现预测和决策建模功能、社交网络的信息匹配技术、机器学习和人工智能应用等。 是Web开发者、架构师、应用工程师等的绝佳选择。-Book and calculation of statistical machine learning as the theme background, specifically on how to mine and analyze data and resources on the Web, how to analyze the user experience, marketing, personal tastes, and many other information, and draw useful conclusions, through complex algorithms Obtained from the Web site to collect and analyze data and user feedback in order to create new customer value and business value. Informative book, including collaborative filtering technology (associated with product recommendation capabilities to achieve), the cluster data analysis (in large-scale data set to explore a subset of similar data), the core search engine technology (crawler, index, query engine, PageRank algorithm, etc.) Massive search for statistical information and analysis concluded that the optimization algorithms, Bayesian filtering technology (spam filtering, text filtering), using decision tree technology for forecasting and decision modeling, social network information ma