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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/predictive-analytics
课程评论：没有评论
课程名称：实用预测分析：模型与方法 课程概述：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。课程还将探讨在解释统计论证时常见的误区，尤其是与大数据相关的误区。通过学习，您将内化一套实用且有效的机器学习方法和概念，并将其应用于解决一些现实问题。 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. 使用重抽样方法提出明确且可靠的统计论证，而无需复杂的符号。 3. 解释并应用一套逐渐复杂的分类方法（规则、决策树、随机森林），及相关的优化方法（梯度下降及其变体）。 4. 解释并应用一系列无监督学习的概念和方法。 5. 描述大规模图分析的常见习惯，包括结构查询、遍历和递归查询、PageRank，以及社区检测。 教学大纲： 第1部分：实用统计推断 描述：学习统计推断的基础知识，将经典方法与重抽样方法进行比较。通过当前科学基础话题（如发表偏倚和可重复性）来激励学习。 第2部分：监督学习 描述：了解机器学习中重要的方法、算法和技术，学习这些方法如何相互构建并结合成能够在多种任务中表现良好的实用算法。学习如何评估机器学习方法及应避免的误区。 第3部分：优化 描述：学习如何使用梯度下降优化成本函数，包括使用随机化和并行化的流行变种以提高性能，培养对实践中常用方法的直觉，并了解其基本相似性。 第4部分：无监督学习 描述：简要介绍一些无监督学习方法，并有机会将技术应用于实际问题。
Part: 1
Title:Practical Statistical Inference
Description: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.
Part: 2
Title:Supervised Learning
Description:Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
Part: 3
Title:Optimization
Description:You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
Part: 4
Title:Unsupervised Learning
Description:A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
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
