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Avis et commentaires pour d'étudiants pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
étoiles
3,063 évaluations
580 avis

À propos du cours

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....

Meilleurs avis

MR
13 août 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 févr. 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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551 - 571 sur 571 Avis pour Apprentissage mechanique pratique

par Michael R

3 oct. 2019

It's a mediocre intro to some machine learning tools. I think the course materials could be drastically improved.

par Philip E W J

30 janv. 2019

Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.

par Allister G A

25 déc. 2017

The course needs to elaborate more on hands on discussions.

par max

18 janv. 2017

not what I expected for a machine learning course

par Y. B

6 févr. 2016

incomplete and not clear. extremely disappointed.

par Yang L

14 août 2016

needs more case studies and examples

par Haolei F

13 mars 2016

Need to get more in-depth

par Naman D D

31 août 2020

Very vague as a mooc.

par Gianluca M

20 oct. 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.

par 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.

par Ricardo G C

17 juin 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.

par Danielle S

22 mars 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.

par Jo S

4 févr. 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.

par Robert O

6 avr. 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.

par Etienne B

1 mars 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.

par Eduardo S B

26 janv. 2020

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

par Abhilash R N

4 déc. 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

par Emily S A

25 mai 2020

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

par yi s

19 juil. 2016

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

par Stephen E

27 juin 2016

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

par Stephane T

31 janv. 2016

Too much surface, not enough depth.