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

워싱턴 대학교의 Practical Predictive Analytics: Models and Methods 학습자 리뷰 및 피드백

4.1
290개의 평가
55개의 리뷰

강좌 소개

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

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.

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:

Practical Predictive Analytics: Models and Methods의 53개 리뷰 중 26~50

교육 기관: Nathaniel E

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.

교육 기관: Alon M

Jan 15, 2018

rather nice course. learn R before joining

교육 기관: Antonio P L

Jan 08, 2016

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

교육 기관: Roberto S

Jun 13, 2017

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

교육 기관: William L K

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.

교육 기관: Jason M

Dec 19, 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.

교육 기관: Ashish S

Jun 29, 2019

Was expecting more to learn on stats and R.

교육 기관: Guido T

Jan 21, 2016

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

교육 기관: Sasa L

Jul 17, 2016

Content is too easy

교육 기관: Faisal G

Nov 21, 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.

교육 기관: Harald B

Mar 17, 2016

the "practical" part is not really existent

교육 기관: Solvita B

Mar 17, 2016

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

교육 기관: Benjamin F

Feb 04, 2018

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

교육 기관: Lucas S

Mar 15, 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.

교육 기관: Robert H S J

Feb 15, 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.

교육 기관: Praketa S

Nov 07, 2016

it gets on my nerve from 3rd Work onwards

교육 기관: Andrew T

Jan 12, 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.

교육 기관: Sambed A

Dec 25, 2015

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

교육 기관: Jonas C

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.

교육 기관: Ben K

May 27, 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.

교육 기관: Lei Z

Mar 22, 2017

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

교육 기관: Jana E

Dec 07, 2017

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

교육 기관: Qianfan W

May 09, 2016

Do not like the slides and the way it is explained. Compared with other ML courses on cousera, this one makes me feel that it is more like a handbook/dictionary instead of a tutorial to teach students. If you already know it, it would help you refresh the mind. Otherwise, you might find it is just to show off how how complex and mysterious is the data science.

교육 기관: Sajit K

Feb 13, 2016

Unrelated and incohesive lectures. Disappointed. Lots of random topics talked about .but nothing in depth.

교육 기관: Andre J

Jun 21, 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).