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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
2,605 classificações
491 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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401 — 425 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Chuxing C

Feb 05, 2016

the lack of assisted practices made it harder to digest the contents and methodologies.

strongly suggest to develop some practice problems with explanations.

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 Matias T

Apr 06, 2016

In my view the course was useful but not as good as the previus ones I followed in the specializacion (such as regression models and stat. inference).

The subject was too broad and there was no space to cover in detail all the algorithms. Also I think it's a bit out of date because there is no references to xgbboost which is now dominating many Kaggle contests

por Matthias H

Mar 26, 2016

The quizes do not match a 100% with the lecture videos. There are some weird questions. My algorithms' outputs deviate from answers some times, which is due to different software versions. Quizes are not very educating this time. Courses by Brian Caffo were much better.

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 Fernando M

Feb 03, 2016

Class materials and videos are confusing and do not go into enough detail. Assignments require a lot of search of extra information outside course materials. Also, the length that is needed to complete the assignments vary widely week to week.

por Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

por Samy S

Apr 23, 2016

As as standalone course on machine learning, it's probably best to take Andrew Ng's class on Coursera. This course mostly teaches the basic usage of the caret package. It is too short to cover more fundamental topics in machine learning, like how to choose an algorithm based on the problem and the data.

I took this class just because I was engaged in the Data Science specialization. I wanted to clear the Capstone project and get the Data Science specialization certificate.

por Ada

Nov 14, 2016

Although again very interesting, I found the lack of additional materials such as practical exercises, swirls and a book reduced the depth of the course knowledge for me. Maybe we have been spoiled by the previous courses :-)

por Dheeraj A

Jan 18, 2016

I believe this course is critical and much needed given where the Industry is heading. Prof Leek, has tried his best to explain the concepts in a lucid manner, however the complexity of the content, may challenge most students.

A few more examples with R code would have been helpful as translating problem statement to R code may not be intuitive.

I would highly recommend that students should plan to study some advance statistics before attempting this course. Having said that, i think this is a wonderful starter course to get a glimpse of what Machine Learning is all about.

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 Robert C

Aug 01, 2017

This course needs swirl assignments. I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

por Rafael M

Mar 30, 2016

The course feels rushed. I understand teaching Machine Learning in 4 weeks is impossible, but then maybe the course needs to have a narrower yet deeper scope rather than throw at you many concepts without details. e.g. trees, random forests, bagging and boosting all in 10 minutes each? Impossible...

So, as opposed to creating machine learning intuition I feel the course became an R package code book.

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 Baha`a A D

Oct 20, 2016

Good enough to open up mind of researcher

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 Miguel J d S P

May 19, 2017

I didn't enjoy the supporting materials and the quizzes weren't very interesting. The final project was fine.

The subject is super interesting.

por Henrique C A

Oct 14, 2016

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