Back to Practical Predictive Analytics: Models and Methods
University of Washington

Practical Predictive Analytics: Models and Methods

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

Status: Classification Algorithms
Status: R Programming
Course8 hours

Featured reviews

RS

4.0Reviewed Jun 12, 2017

Very good approach to each method; the assignments are a good test for the topics.

NE

4.0Reviewed Jun 7, 2017

I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.

WL

5.0Reviewed Jun 5, 2016

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.

HD

4.0Reviewed Aug 30, 2016

The entire course is an overview! This course will be a revision if you already know the concepts.

SP

5.0Reviewed Dec 22, 2016

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

GJ

5.0Reviewed Jul 16, 2021

This course helpemd me understand more about machine learning and a set of tools to help with the same.

SM

5.0Reviewed Feb 23, 2016

Professor Bill Howe gives great reactions to when there are typos on the slides!

TR

5.0Reviewed Feb 16, 2016

Its a great review course. Prior knowledge is necessary

KR

5.0Reviewed Nov 10, 2015

Very nice assignments and content. You learn a lot when you complete all assignments.

WK

4.0Reviewed Jun 5, 2017

Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.

PV

5.0Reviewed Nov 11, 2015

The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course

AS

5.0Reviewed Nov 23, 2015

Excellent course with amazing practical exercises!

All reviews

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Jonas Carvalho
2.0
Reviewed Apr 18, 2017
W QF
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Reviewed May 9, 2016
Yifei Gong
5.0
Reviewed Jun 26, 2019
Seema Pinto
5.0
Reviewed Dec 23, 2016
Kenneth Penza
5.0
Reviewed Feb 8, 2016
Prasad Vaidya
5.0
Reviewed Nov 12, 2015
Chen Yang
5.0
Reviewed Jul 20, 2016
Weng Lee
5.0
Reviewed Jun 6, 2016
Giby James
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Reviewed Jul 17, 2021
Bingcheng Luo
5.0
Reviewed Aug 7, 2019
Kevin Raetz
5.0
Reviewed Nov 11, 2015
Shota Makino
5.0
Reviewed Feb 24, 2016
Dr. Balwant A. Sonkamble
5.0
Reviewed Jul 3, 2020
Francisco Yllera
5.0
Reviewed Jan 18, 2016
Tamal Roy
5.0
Reviewed Feb 17, 2016
Artur Sagitov
5.0
Reviewed Nov 24, 2015
Shivanand R Koppalkar
5.0
Reviewed Jun 18, 2016
Jigyasa Bisariya
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Reviewed Sep 19, 2022
Menghe Lu
5.0
Reviewed Jun 12, 2017
Pankaj Agarwal
5.0
Reviewed Jul 14, 2021