Volver a Practical Predictive Analytics: Models and Methods

4.1

287 calificaciones

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53 revisiones

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...

por 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.

por 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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51 revisiones

por Yogesh Baliram Naik

•Feb 20, 2019

Nice course

por Anand Prakash

•Feb 11, 2019

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por 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.

por Benjamin Farcy

•Feb 04, 2018

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

por Alon Mann

•Jan 15, 2018

rather nice course. learn R before joining

por Jana Endemann

•Dec 07, 2017

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

por Sergio Garofoli

•Oct 30, 2017

Excellent!!

por Roberto Santamaria

•Jun 13, 2017

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

por Menghe Lu

•Jun 12, 2017

great for learner

por 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.

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