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University of Washington

Practical Predictive Analytics: Models and Methods

University of Washington via Coursera

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Overview

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

Taught by

Bill Howe

Reviews

2.0 rating, based on 3 Class Central reviews

Start your review of Practical Predictive Analytics: Models and Methods

  • Profile image for Marcio Gualtieri
    Marcio Gualtieri

    Marcio Gualtieri completed this course, spending 3 hours a week on it and found the course difficulty to be very easy.

    This course is quite outdated. I didn't learn much beyond what I already knew before I started. The Spark courses from edX are way better than these. Hopefully "Big Data Analysis with Scala and Spark" from the "École Polytechnique Fédérale de Lausan…
  • Colin Khein completed this course.

  • Janet Wesner completed this course.

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