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Kursteilnehmer-Bewertung und -Feedback für Practical Machine Learning von Johns Hopkins University

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3,058 Bewertungen
580 Bewertungen

Über den Kurs

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Top-Bewertungen

MR
13. Aug. 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

AD
28. Feb. 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

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501 - 525 von 570 Bewertungen für Practical Machine Learning

von Manuel E

8. Aug. 2019

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

von Noelia O F

19. Juli 2016

Good course for learning the basics of the caret package. However, it is not a good course for learning machine learning.

von Joseph I

1. Feb. 2020

Material was very interesting but was covered at a very high level and a lot of additional learning was required.

von José A G R

5. Feb. 2017

Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python

von BAUYRJAN J

1. März 2017

Instructor rushes the course and does not explain much in the same level of details as respective quiz requires

von Hongzhi Z

2. Jan. 2018

All the formulas and code in slides are too abstract. If can be more charts to interpret that will be better.

von Henrique C A

13. Okt. 2016

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

von Alex F

29. Dez. 2018

A fine introduction, but there are much more engaging and better quality courses out there...

von Yingnan X

11. Feb. 2016

If you have taken Andrew Ng's machine learning class, it's not necessary to take this one.

von Yohan A H

6. Sep. 2019

I think it was a very fast course and I feel more real examples would have been useful,

von fabio a a l l

14. Nov. 2017

Poor supporting material in a course that tries to cover a lot in a very limited time.

von Rafael S

24. Juli 2018

this course seemed too rushed for me, too little content for such a extense subject

von Raj V J

24. Jan. 2016

more needs to be taught in class. what is taught is not sufficient for quizzes.

von Surjya N P

2. Juli 2017

Overally course is good. But weekly programming assignments will be great.

von 王也

17. Dez. 2016

Too different for beginners but not deep enough for ones already know R.

von james

10. Sep. 2016

Quizzes are useful exercises but need to do a lot of self studying.

von Philip A

26. Feb. 2017

mentorship was great, but the video lectures were almost useless.

von Christoph G

4. Dez. 2016

The topic is too big, for one course from my point of view.

von Ariel S G

27. Juni 2017

In my opinion, this course needs a few extra exercises.

von Jorge L

13. Okt. 2016

Fair but assignments are not very well explained

von Bahaa A

20. Okt. 2016

Good enough to open up mind of researcher

von Johnnery A

20. März 2020

I need study more this course

von Sergio R

20. Sep. 2017

I miss Swirl

von Serene S

29. Apr. 2016

too easy

von Estrella P

7. Juli 2020

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