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Practical Predictive Analytics: Models and Methods, University of Washington

4.1
280 ratings
50 reviews

About this Course

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...

Top reviews

By SP

Dec 23, 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.

By KP

Feb 08, 2016

I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .

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48 Reviews

By Benjamin Farcy

Feb 04, 2018

Meh, if you want to really dive in predictive analytics go to other courses.

By Alon Mann

Jan 15, 2018

rather nice course. learn R before joining

By Jana Endemann

Dec 07, 2017

Same as before, subjects are quite interesting, but the video material is of quite low quality.

By Sergio Garofoli

Oct 30, 2017

Excellent!!

By Roberto Santamaria

Jun 13, 2017

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

By Menghe Lu

Jun 12, 2017

great for learner

By Nathaniel Evans

Jun 08, 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.

By William L. Koch

Jun 06, 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.

By Jonas Carvalho

Apr 19, 2017

The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.

By Lei Zhang

Mar 22, 2017

The course is good. But it does not has lecture slides that is better for students to understand.