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Avis et commentaires pour d'étudiants pour Practical Predictive Analytics: Models and Methods par Université de Washington

307 évaluations
58 avis

À propos du cours

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...

Meilleurs avis


22 déc. 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.


7 févr. 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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26 - 50 sur 56 Avis pour Practical Predictive Analytics: Models and Methods

par Jason M

19 déc. 2015

Excellent crash course in machine learning and introduction to the kaggle data science competitions. However, the grading system had bugs and was unable to accept two answers as correct making it very frustrating. The grader was finally fixed so next round of this course should be a better experience.

par Kairsten F

26 oct. 2016

This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.

par Nathaniel E

8 juin 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.

par William L K

6 juin 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.

par Harini D

31 août 2016

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

par Roberto S

13 juin 2017

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

par Nico G

22 déc. 2015

Very interesting course. It would be useful to download slide used during videos.

par Antonio P L

8 janv. 2016

Great Course but the assigment don't show the understanding of the course

par Zoltan P

23 déc. 2015

More dynamic visualisation please, and it will be 5*.

par Ashish S

29 juin 2019

Was expecting more to learn on stats and R.

par Alon M

15 janv. 2018

rather nice course. learn R before joining

par Jiancheng

25 févr. 2016

good course material!

par Andrew T

12 janv. 2016

The lectures in this course were very good but I would have preferred much, much more homework to practice the concepts covered in the lectures.

Also, I was somewhat disappointed when a certain issue with the course that I asked about in the forums was never addressed by the course staff. Of course, I could have been wrong about it but, but based on the response from other students I was not the only one having this problem.

par Lucas S

15 mars 2017

Great overview of many models and techniques, but very high level. Would have greatly benefited from links to resources to learn more about all the subjects. This course leaves students with only basic knowledge of the subject matter, which is fine considering the course timeline. But, for those who want to explore further please recommend sources of additional reading and research.

par Robert H S J

15 févr. 2016

This course was in some ways a disappointment. Although the lectures were intriguing and clear, I felt like the assignments were essentially "Go and pick up R on your own," which was pretty frustrating.

par Faisal G

20 nov. 2016

I felt that topics were not treated in enough depth. It was a lot of topics to cover in a 4-week course.

I learned a lot from the kaggle competition.

par Guido T

21 janv. 2016

Interesting course and specialization. A few inaccuracies need to be corrected so it can be properly pursued at its best.

par Solvita B

17 mars 2016

Problems with vitual machine for R assigment. For peer review detail evaluation guidelines is need.

par Sambed A

24 déc. 2015

It's a decent course. Not as thorough as Analytics Edge or Machine Learning (by Andrew Ng).

par Benjamin F

4 févr. 2018

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

par Harald B

17 mars 2016

the "practical" part is not really existent

par Praketa S

7 nov. 2016

it gets on my nerve from 3rd Work onwards

par Sasa L

17 juil. 2016

Content is too easy

par Andre J

21 juin 2016

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments.

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

par Ben K

27 mai 2016

This course probably deserves 3-4 stars in a better, maintained form, but the entire specialization is not maintained, the lectures have no production values. Basically, it's a money pit that Coursera is keeping up cynically. It's a real shame because the syllabus correctly addresses a gap in most data scientists' skills.