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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
2,610 classificações
492 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.

AS

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

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376 — 400 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por carlos j m r

Oct 05, 2017

I thought there were Swirl practice as other courses, however this course is very good.

por Erika G

Jul 28, 2016

I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.

por Md F A

Aug 14, 2017

To me with this course, the best learning aspect is the final project; how to use Machine Learning Algorithms on data analysis.

por Brandon K

Mar 30, 2016

The lectures were great and engaging. I felt like they went too fast. Jeff says at the beginning that this is just an overview and points to some other resources. As an overview, this class works well. You can expect to learn a bit about what machine learning is and how to to do it using the caret package in R.

por Robert K

Nov 14, 2017

I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course! Still, much benefit and many useful concepts covered which can be revisited in greater detail down the track.

I would also like to see the final assignment change subtly every so often as there are existing completions on the web and it's too easy/tempting for some to simply copy and paste.

por Daniel R

May 14, 2016

The course is really great, however it should last a little longer, 4 weeks is hard to accomplish

por Chonlatit P

Oct 20, 2018

GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.

por Jakub W

Sep 24, 2018

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

por Johnny C

Oct 23, 2018

It was in general nice course. However, quizzes need improvement.

por Samuel Q

Oct 24, 2018

Good course to get only the basics of machine learning. The assignments and quizzes are great but the lecture material is very brief and short. The references provided throughout the lectures are probably the best source of more information.

por César A

Jul 26, 2018

Very interesting course. May be a little bit harder than the previous ones but it could be done.

por Jiarui Q

Mar 27, 2019

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

por Sanket P

May 27, 2019

ok

por Erik K

Jul 08, 2019

Very good. Learned a lot

por Oliver S

Jul 26, 2019

A reference solution for the quiz questions as there are in some other courses in this specialization would have been nice, since I got sometimes very different results using the newest versions of the libraries and I'd really like to know, if I made any big mistakes and it's not only because of my setup.

por Caio H

Aug 23, 2019

I learned a lot in this course, but I would recommend taking the courses in order.

por Robert S

Sep 16, 2019

The lecture material is great, but the quiz material is in need of updating. R and it's packages have gone through many updates since the problems were written so it is sometimes difficult to reproduce their results even with running the sample codes given after getting the answer correct.

por 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.

por 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.

por Paul R

Mar 13, 2019

A key course everything has been building towards, some important concepts and modeling techniques are introduced. However Jeff rushes through a lot of material, and I think this would be better served as two courses with more case studies and exercises, especially as the capstone doesn't use much of this. But nevertheless a useful introduction to this topic, concepts of training vs. testing etc, different models to be used, along with the caret package in R.

por Alex F

Dec 30, 2018

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

por 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.

por Sergio R

Sep 20, 2017

I miss Swirl

por Romain F

Sep 02, 2017

Like all courses in the specialization, good introduction to statistical learning, although a bit rushed off.

The learner has to navigate through the arcanes of r packages, which is not always easy. I am also quite surprised that neural networks are not part of the course, it should be disclaimed in the course content.

por 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.