Data Mining (UI. Urbana-Champaign)<\/u><\/strong><\/a><\/p>\n The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.<\/p>\n Master Recommender Systems (U Minnesota)<\/u><\/strong><\/a><\/p>\n This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.<\/p>\n Machine Learning (Stanford)<\/u><\/strong><\/a><\/p>\n \u672c\u8ab2\u7a0b\u5c07\u5ee3\u6cdb\u4ecb\u7d39\u6a5f\u5668\u5b78\u7fd2\u3001\u6578\u64da\u6316\u6398\u548c\u7d71\u8a08\u6a21\u5f0f\u8b58\u5225\u3002\u76f8\u95dc\u4e3b\u984c\u5305\u62ec\uff1a(i) \u76e3\u7763\u5f0f\u5b78\u7fd2\uff08\u53c3\u6578\u548c\u975e\u53c3\u6578\u7b97\u6cd5\u3001\u652f\u6301\u5411\u91cf\u6a5f\u3001\u6838\u51fd\u6578\u548c\u795e\u7d93\u7db2\u7d61\uff09\u3002 (ii) \u7121\u76e3\u7763\u5b78\u7fd2\uff08\u96c6\u7fa4\u3001\u964d\u7dad\u3001\u63a8\u85a6\u7cfb\u7d71\u548c\u6df1\u5ea6\u5b78\u7fd2\uff09\u3002 (iii) \u6a5f\u5668\u5b78\u7fd2\u5be6\u4f8b\uff08\u504f\u898b\/\u65b9\u5dee\u7406\u8ad6\uff1b\u6a5f\u5668\u5b78\u7fd2\u548cAI\u9818\u57df\u7684\u5275\u65b0\uff09\u3002\u8ab2\u7a0b\u5c07\u5f15\u7528\u5f88\u591a\u6848\u4f8b\u548c\u61c9\u7528\uff0c\u60a8\u9084\u9700\u8981\u5b78\u7fd2\u5982\u4f55\u5728\u4e0d\u540c\u9818\u57df\u61c9\u7528\u5b78\u7fd2\u7b97\u6cd5\uff0c\u4f8b\u5982\u667a\u80fd\u6a5f\u5668\u4eba\uff08\u611f\u77e5\u548c\u63a7\u5236\uff09\u3001\u6587\u672c\u7406\u89e3\uff08\u7db2\u7d61\u641c\u7d22\u548c\u5783\u573e\u90f5\u4ef6\u904e\u6ffe\uff09\u3001\u8a08\u7b97\u6a5f\u8996\u89ba\u3001\u91ab\u5b78\u4fe1\u606f\u5b78\u3001\u97f3\u983b\u3001\u6578\u64da\u5eab\u6316\u6398\u7b49\u9818\u57df\u3002<\/p>\n Probabilistic Graphical Models (Stanford)<\/u><\/strong><\/a><\/p>\n Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.<\/p>\n <\/p>\n Big Data Analysis with Apache Spark (UC. Berkeley)<\/u><\/strong><\/a><\/p>\n Learn how to apply data science techniques using parallel programming in Apache Spark to explore big data.<\/p>\nBig Data Platform<\/h1>\n