University of Washington
Practical Predictive Analytics: Models and Methods

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University of Washington

Practical Predictive Analytics: Models and Methods

Bill Howe

Instructor: Bill Howe

37,754 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.1

(319 reviews)

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.1

(319 reviews)

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

1 assignment

Taught in English

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This course is part of the Data Science at Scale Specialization
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There are 4 modules in this course

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.

What's included

28 videos

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.

What's included

26 videos1 reading1 assignment

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.

What's included

11 videos

A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.

What's included

4 videos1 peer review

Instructor

Bill Howe
University of Washington
4 Courses89,042 learners

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Recommended if you're interested in Data Analysis

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Showing 3 of 319

4.1

319 reviews

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BL
5

Reviewed on Aug 6, 2019

RS
4

Reviewed on Jun 12, 2017

WE
4

Reviewed on Feb 3, 2017

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