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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

状态:Machine Learning Algorithms
状态:Supervised Learning
课程小时

精选评论

RS

4.0评论日期:Jun 12, 2017

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

NE

4.0评论日期: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

5.0评论日期:Jun 5, 2016

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

HD

4.0评论日期:Aug 30, 2016

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

SP

5.0评论日期: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

5.0评论日期:Jul 16, 2021

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

SM

5.0评论日期:Feb 23, 2016

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

TR

5.0评论日期:Feb 16, 2016

Its a great review course. Prior knowledge is necessary

KR

5.0评论日期:Nov 10, 2015

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

WK

4.0评论日期: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

5.0评论日期:Nov 11, 2015

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

AS

5.0评论日期:Nov 23, 2015

Excellent course with amazing practical exercises!

所有审阅

显示:20/59

Jonas Carvalho
2.0
评论日期:Apr 18, 2017
W QF
2.0
评论日期:May 9, 2016
Yifei Gong
5.0
评论日期:Jun 25, 2019
Seema Pinto
5.0
评论日期:Dec 22, 2016
Kenneth Penza
5.0
评论日期:Feb 8, 2016
Prasad Vaidya
5.0
评论日期:Nov 12, 2015
Chen Yang
5.0
评论日期:Jul 20, 2016
Weng Lee
5.0
评论日期:Jun 6, 2016
Giby James
5.0
评论日期:Jul 17, 2021
Bingcheng Luo
5.0
评论日期:Aug 7, 2019
Kevin Raetz
5.0
评论日期:Nov 11, 2015
Shota Makino
5.0
评论日期:Feb 24, 2016
Dr. Balwant A. Sonkamble
5.0
评论日期:Jul 3, 2020
Francisco Yllera
5.0
评论日期:Jan 18, 2016
Tamal Roy
5.0
评论日期:Feb 17, 2016
Artur Sagitov
5.0
评论日期:Nov 23, 2015
Shivanand R Koppalkar
5.0
评论日期:Jun 18, 2016
Jigyasa Bisariya
5.0
评论日期:Sep 18, 2022
Menghe Lu
5.0
评论日期:Jun 12, 2017
Pankaj Agarwal
5.0
评论日期:Jul 14, 2021