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

2,890 évaluations
547 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


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.


Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

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476 - 500 sur 539 Avis pour Apprentissage mechanique pratique

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


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Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

par Alex F

Dec 30, 2018

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

par Yingnan X

Feb 11, 2016

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

par Yohan A H

Sep 06, 2019

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

par fabio a a l l

Nov 14, 2017

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

par Rafael d R S

Jul 24, 2018

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

par Raj V J

Jan 24, 2016

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

par Surjya N P

Jul 03, 2017

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

par 王也

Dec 18, 2016

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

par james

Sep 10, 2016

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

par Philip A

Feb 27, 2017

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

par Christoph G

Dec 04, 2016

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

par Ariel S G

Jun 27, 2017

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

par Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

par Bahaa A

Oct 20, 2016

Good enough to open up mind of researcher

par Johnnery A

Mar 20, 2020

I need study more this course

par Sergio R

Sep 20, 2017

I miss Swirl

par Serene S

Apr 29, 2016

too easy

par Estrella P

Jul 07, 2020


par Miguel C

May 10, 2020

I really enjoyed the content of the course. I already knew a fair amount about machine learning but I learned a lot more than I thought I would. Most contents of weeks 3 and 4 - decision trees and random forests, bagging and boosting, linear discriminant analysis and naive Bayes, forecasting and unsupervised predictions - were my favourite topics in this course.

The biggest disappointment in this course for me were the outdated quizzes. I worked really hard through this course and most of the Data Science specialisation. But the quizzes are set up for older versions of R and some of its packages, so the results are completely different from what I got most of the time. I found this extremely frustrating and disheartening and had to repeat the quizzes several times. I do realise that most quizzes enumerate at the beginning the versions they are using, but there is no mention of how one goes about to set that up in R. On top of that, given that I rarely passed the quiz on the first try my Skill Tracking score dropped considerably, undermining weeks and weeks of hard work.

Unfortunately, this tainted my view of this course and I would advise the course organisers to update it as soon as possible.

par Michael S

Feb 07, 2016

Had big expectations for this one... really one of the ones to look forward to after working through the beginning of the specialization, but for some reason, it seemed any prof or even TA interaction was absent this time around like in none of the other specialization coursed to date. Bugs in the new interface and quizzes weren't really addressed. Couldn't even get an official response about the apparent removal of Distinction-level now (which I'd been working to get in all specialization courses and now seems no longer an option). Still interesting content. As a "free" course, it's still really valuable. As one of the people that paid for this and all others in this specialization, this is the one I felt didn't return as much value to justify the payment with no "official" course staff seeming to be involved this round.

par Agatha L

Jan 23, 2018

I was disappointed with this course. For better or worse ML is a part of data science and, in this course, the instructional depth was lacking. The lectures provided examples of how to implement a few ML algorithms in R, with very little actual instruction on the intricacies of these algorithms, theoretical foundations etc. Taking the course I felt somewhat cheated (a google search would have done the job of the class), and frustrated with various little bugs in Quiz/Assignment content.