实用预测分析:模型与方法

Practical Predictive Analytics: Models and Methods

大规模数据科学专项课程,4 课程序列的一部分,

华盛顿大学

Coursera

计算机

普通(中级)

13 小时

  • 英语, 韩语
  • 436

课程概况

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

你将学到什么

Random Forest

Predictive Analytics

Machine Learning

R Programming

课程大纲

周1
完成时间为 2 小时
Practical Statistical Inference
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.
28 个视频 (总计 121 分钟)

周2
完成时间为 2 小时
Supervised Learning
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.
26 个视频 (总计 111 分钟), 1 个阅读材料, 1 个测验

周3
完成时间为 1 小时
Optimization
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.
11 个视频 (总计 41 分钟)

周4
完成时间为 2 小时
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.

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