Chevron Left
Practical Predictive Analytics: Models and Methods(으)로 돌아가기

Practical Predictive Analytics: Models and Methods, 워싱턴 대학교

4.1
(287개의 평가)

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...

최상위 리뷰

대학: 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.

대학: 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 .

필터링 기준:

51개의 리뷰

대학: Yogesh Baliram Naik

Feb 20, 2019

Nice course

대학: Anand Prakash

Feb 11, 2019

V

e

r

y

g

o

o

d

대학: Yifei Gong

Jan 03, 2019

I can feel Prof. Howe tried to cover as much as possible and to build a foundation for both practicing as well as further study on the topics. However, I do feel it is not patient enough to give a detailed yet easy-to-follow explanation for some of the topics, and I had to do quite some self-readings to close the gap. I think it will be helpful if the course can provide some reading materials on how some of the formulas are derived (e.g. gradient descent, logistic regression etc.) as a supplement.

대학: Benjamin Farcy

Feb 04, 2018

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

대학: Alon Mann

Jan 15, 2018

rather nice course. learn R before joining

대학: Jana Endemann

Dec 07, 2017

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

대학: Sergio Garofoli

Oct 30, 2017

Excellent!!

대학: Roberto Santamaria

Jun 13, 2017

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

대학: Menghe Lu

Jun 12, 2017

great for learner

대학: 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.