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

4.5
étoiles
3,022 évaluations
573 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

Aug 14, 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

Mar 01, 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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476 - 500 sur 565 Avis pour Apprentissage mechanique pratique

par Ivana L

Feb 24, 2016

Compared to previous two courses in specialization this one is far worse - it is more of excursion into used methods than actual learning using any of mentioned methods in enough detail to be able to do meaningful analyses.

par CHEN X

Dec 03, 2015

Feels like everything is solved using a caret package, while the back-end theory is only slightly touched. By using a single line command solver, student may lack the foundation for harder problems in the real world.

par Daniel J R

Jan 17, 2019

Seems like a lot to pack into 4 -weeks. Should really be named introductory machine learning. Needs more depth and better development of the intuitions associated to each algorithm class to match the expectations.

par Ayushmaan D V

Aug 17, 2020

The material covered was good and informative, the reference material was nice. But the video leactures themselves were lacking in many respects. The videos covered only a bare minimum and could have been longer.

par Vinay K S

Feb 19, 2017

I like initial courses like Exploratory Data Analysis but later on it got harder to follow the lectures. A lot of topics were just rushed through and little effort was made to make them engaging or interesting.

par Andrew W

Mar 13, 2018

Very interesting subject area, I think there is simply too much to cram into one course. Should consider spliting the subject into 2 courese or simply concentrate on only 1 or 2 main areas (e.g. cla

par Andrew W

Feb 10, 2017

The videos are really tutorials on R functions for machine learning and data wrangling. A good substitute for "Machine Learning" by Andrew Ng in terms of managing data sets and exploratory analysis.

par M. D

Jul 11, 2020

Content somewhat outdated. Referenced packages don't always work in current version of R. Material can be better explained with more detailed discussion of examples rather than theory.

par Robert C

Aug 01, 2017

This course needs swirl assignments. I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

par Raul M

Feb 12, 2019

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

par Brian F

Aug 16, 2017

There was some good material in here, but it was rushed and is deserving of a much longer course - especially compared to some of the other modules in this course.

par Chuxing C

Feb 05, 2016

the lack of assisted practices made it harder to digest the contents and methodologies.

strongly suggest to develop some practice problems with explanations.

par Michalis F

May 26, 2017

Good in introducing caret package and getting some experience in running algorithms. Was expecting more in-depth discussion about the methods though.

par Davin G

Aug 26, 2019

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

par Léa F

Jan 09, 2018

Rather good overview. The contents could dig deeper into each subject, and it would improve the course a lot if some exercises in Swirl were added.

par Miguel J d S P

May 19, 2017

I didn't enjoy the supporting materials and the quizzes weren't very interesting. The final project was fine.

The subject is super interesting.

par Max M

Dec 12, 2017

Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.

par Kyle H

May 09, 2018

A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.

par Manuel E

Aug 08, 2019

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

par Noelia O F

Jul 19, 2016

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

par Joseph I

Feb 01, 2020

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

par José A G R

Feb 05, 2017

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

par BAUYRJAN J

Mar 01, 2017

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

par Hongzhi Z

Jan 03, 2018

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

par Henrique C A

Oct 14, 2016

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