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$79

Practical Predictive Analytics: Models and Methods

Created by -

Bill Howe
,
University of Washington

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English

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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

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USD 79

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Type: Online

This course includes

  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

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course image

USD 79

provider image

Type: Online

This course includes

  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

Taken this course?

Share your experience with other students

Share

Add Review

Practical Predictive Analytics: Models and Methods

Created by -

Bill Howe
,
University of Washington

0.00

(0 ratings)

All Levels

Start Date: February 23rd 2021

Course Description

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

The information used on this page is how each course is described on the Coursera platform.

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Bill Howe,University of Washington

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