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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
2,667 classificações
500 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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401 — 425 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

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.

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 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.

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 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 Christian B

Apr 30, 2017

First I want to thank very much the instructor in the online forum. He helped me a lot at the end of the course and his tutorials for gh-pages are excellent. He was also very fast in responding. Thank you.

The course did ultimately not really gave me what I was looking for. Maybe too may different facts and not enough depth. I am not sure that I can confidently say that I can build a ML model now. Technically I can, but the deeper understanding is missing. For example: When would I use which method (for example rf versus naive base), the last exercise about cross validation was not fully clear. Using the caret package is too high level for a learner. It would be better to see some more step by step examples. It was not clear to me what the expected error calculation in the last exercise was really looking at. Maybe what is missing a swirl exercises, not using caret. and then explaining how caret can simplify it. We also learned how to create a predictive model, but did not go into how the model gets updated and gets retrained, an important aspect of ML. i also do not see unsupervised learning to be covered.

por 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

por 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.

por Ariel S G

Jun 27, 2017

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

por Christopher B

Mar 01, 2017

While the overview of the content seemed very reasonable both in scope and pacing, the lack of swirl exercises meant that the final project for the course was a bit jostling. Overall, I think this course still needs some development in the way of exercises to familiarize the student with the practical exercises associated with machine learning.

por Eduardo P

Apr 14, 2017

This is such a cornerstone topic to the Data Science Specialization that I think it deserves a better designed and more polished curriculum. The subject is so extensive that it might be worthy to split the contents in two courses. Finally, I would like to suggest the authors of the course modeling the curriculum following the amazing treatment of the subject found in "Introduction to Statistical Learning" by Hastie, Tibshiriani et. al.

por Christoph G

Dec 04, 2016

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

por 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.

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 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.

por 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.

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 Jorge L

Oct 13, 2016

Fair but assignments are not very well explained