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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
2,675 classificações
501 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

JC

Jan 17, 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

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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351 — 375 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Hernan S

Dec 13, 2016

The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.

por Manuel C

Dec 26, 2017

I feel I could have master the subjects better

por Moiz

Feb 03, 2017

By using the caret package, this course took a very pragmatic approach towards machine learning. It demonstrated how to perform all the essential tasks in making the machine (algorithm) learn from data.

In my case, this course required a dedicated time commitment for successful completion. In addition to course lectures, i used the 'Machine Learning with R' book to fill my knowledge gaps. Overall i feels that this course helped me in my journey of gaining a better understanding of this subject.

por Raymond M

May 02, 2018

pretty good!

por Swapnil A

Jun 09, 2017

The course covers few important topics in R like cross validation, decision trees, random forest etc. which comes in very handy for a data science aspirant. It expects the participant to have a descent knowledge in R. Overall, I am pretty satisfied with this course. Thanks!

por Greig R

Nov 14, 2017

Good course, I learnt a lot. It does need to be updated with more modern versions of software.

por Carlos R

May 27, 2018

Great Course! I have learned a lot of new things.

por Carlos S

Jan 31, 2016

First and foremost I'm so thankful for the exposure to so much material in such a condensed schedule. Very good class. Even though I had to muscle my way through it.

I think the class could be improved with one additional discussion thread for the project.

A guide similar to the ones created for Inferential Statistics and Regression would also have been very helpful.

I benefited immensely from reading parts of the book "An Introduction to Statistical Learning" while taking this course.

por João R

Aug 20, 2017

Got confused how to perform cross validation and when. Other than that, very practical. Great job.

por Lucas

Jun 03, 2016

This course allows you to implement practical solutions using machine learning algorithms without having to know the mechanisms behind the calculations in detail. Unfortunately questions in the discussion forum were quite rare and many questions were not resolved during this course.

por Orest

Jan 22, 2018

It needs more mathematical detail. Otherwise is a fairly comprehensive class, and a great tutorial on the caret package. I recommend it, if you need to refresh concepts and get some practical exposure to caret.

por Alia E

Jul 13, 2018

Really could have used a few more examples.

por Minki J

Dec 29, 2017

good to know many concepts of machine learning model.

por Paul K

Apr 08, 2017

Very good summary of strengths/weaknesses of various machine learning algorithms. This lecturer's style and production quality is much higher than in the previous two courses in the specialization series.

por A. R C

Oct 20, 2017

I enjoyed it but it needs indeed to deep into many concepts, which are just briefly named during the course.

por Matthew C

Dec 11, 2017

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

por KRISHNA R N

Apr 19, 2018

nice

por Carlos M

Jul 12, 2017

A good course, but the field is so large and so important. You'll really need the "hacker" mentality to get through this course. They DO NOT teach you even close to everything you'll need to complete the course. It's also very stats/math heavy which will make the theory difficult. This isn't why I only rated 4 stars. I did so because of the lack of Swirl and the feeling that I still don't feel like I understand the topic well enough to do anything in a business setting yet. I was hoping for more from the class.

por Javier R P

Oct 14, 2017

Love this class !

por marcelo G

Aug 15, 2016

Great course, very demanding, but it could use more reading material, ebooks instead of links on video.

por Kalle H

Jun 25, 2018

Nice course that tries to fit a lot of material into four weeks. Due to this, the material is not so deep, although pointers are given to where the student can find additional information related to each subject covered by the course.

por Sean Q Z

Dec 11, 2016

As the title states, very practical way to show you how this is done in R.

Most of them are lines of codes and some explanation. There are tons of details behind that and remains un-explained.

As other courses in the specialization, students need to do a lot of self-study to further understand machine learning.

But at least, learned a lot.

por BIBHUTI B P

Jul 24, 2017

This was a superb module which created a deep learning insight within me focusing on future technology

por Coral P

Aug 19, 2017

The project is good in letting us practise what we learnt

por Mehul P

Oct 03, 2017

Good ML overview.