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Voltar para Aprendizagem Automática na Prática

Comentários e feedback de alunos de Aprendizagem Automática na Prática da instituição Universidade Johns Hopkins

4.5
estrelas
2,768 classificações
519 avaliações

Sobre o curso

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....

Melhores avaliações

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.

DH

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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451 — 475 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por 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.

por 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.

por 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.

por 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

por BAUYRJAN J

Mar 01, 2017

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

por 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.

por Henrique C A

Oct 14, 2016

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

por Alex F

Dec 30, 2018

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

por Yingnan X

Feb 11, 2016

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

por Yohan A H

Sep 06, 2019

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

por 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.

por Rafael d R S

Jul 24, 2018

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

por Raj V J

Jan 24, 2016

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

por Surjya N P

Jul 03, 2017

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

por 王也

Dec 18, 2016

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

por james

Sep 10, 2016

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

por Philip A

Feb 27, 2017

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

por Christoph G

Dec 04, 2016

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

por Ariel S G

Jun 27, 2017

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

por Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

por Baha`a A D

Oct 20, 2016

Good enough to open up mind of researcher

por Sergio R

Sep 20, 2017

I miss Swirl

por Serene S

Apr 29, 2016

too easy

por 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.

por 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.