Practical Predictive Analytics: Models and Methods

开始时间: 08/08/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew



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Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection

实用的预测分析:模型和方法:统计实验设计和分析是数据科学的核心。在本课程中,您将设计统计实验并使用现代方法分析结果。您还将探索解释统计参数(尤其是与大数据相关的参数)时常见的陷阱。总的来说,本课程将帮助您内化一组实用而有效的机器学习方法和概念的核心,并将其应用于解决一些现实世界中的问题。    学习目标:完成本课程后,您将能够: 1.设计有效的实验并分析结果 2.使用重采样方法使统计参数清晰明了,而无需调用深奥的记号 3.解释并应用一组核心分类方法,这些方法将增加复杂性(规则,树木,随机森林)以及相关的优化方法(梯度下降和变体) 4.解释并应用一组无监督的学习概念和方法 5.描述大规模图形分析的常见习语,包括结构化查询,遍历和递归查询,PageRank和社区检测


Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.





Statistical experiment design and analytics are at the heart of data science. In this course you wi