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

4.5
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3,139 Bewertungen
598 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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576 - 588 von 588 Bewertungen für Practical Machine Learning

von Gianluca M

20. Okt. 2016

Gosh I hated hated hated this course. Nothing to learn here. You will just be given lots of names with no explanation whatsoever.

I often felt really angry at the teacher because of the way he would introduce entire prediction models without explaining anything about them. Also, I really didn't like the fact that the course is centered on caret, a "shortcut" package to do stuff fast. Before doing things fast I need to know what I am doing! Finally, the quizzes and assignments are completely disconnected from the courses.

The worst course I have ever taken on coursera.

von José M M A

25. Mai 2020

This course did not fulfill my expectations. It is the worst one in the Data Science Specialization by far.

Although the explanations are fine, sometimes they are too vague and there is no practice at all, when the title of the course is "Practical".

Most of the tools used are not comprehensively detailed and the quizzes are quite confusing.

Some of my peers reported that the course is not updated since 2013, which is a severe flaw when talking about one of the statistical tools more in-fashion nowadays.

von Ricardo G C

17. Juni 2020

The professors are experts on the subject, but unfortunately they rush through content and some of the classes are outdated (i.e. they use packages and data that are not the newest version) and this generates confusion througout the course.

von Danielle S

22. März 2016

Material is very high level. No ppt's are given, so all links presented in the video's cannot be viewed.

Quizzes are based upon old packages, so incorrect answers are provided.

No replies at discussion board from TA"s or instructors.

von Jo S

4. Feb. 2016

Poor compared with some of the others on this specialisation. The lectures are too fast and high level, with no allowance given for people who are unfamiliar with this area and attempting to learn it.

von Robert O

6. Apr. 2016

Very little depth. I don't recommend this if you don't already have background in statistics or R. I really didn't learn anything. I mostly just gamed the quizzes and projects.

von E B

1. März 2016

Cannot take the exam, I have to pay... wtf... I will probably pay at the end, but I want to take the class first. Without certificate I cannot prove I took the course.

von Eduardo S B

26. Jan. 2020

They explain nothing on the fundamentals of the machine-learning methods, nor how to know which method apply to a given problem.

von Abhilash R N

4. Dez. 2019

This course is NOT for the beginner. Take time to finish all the beginner and foundation courses and then take time to learn R

von Emily S A

25. Mai 2020

In my opiion, this course needs to be improved a lot. There are almost nothing Practical Machine Learning.

von yi s

19. Juli 2016

too general no depth, not recommended for science or engineering degree holders

von Stephen E

27. Juni 2016

To be honest I don't think this is worth the money.

von Stephane T

31. Jan. 2016

Too much surface, not enough depth.