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Avis et commentaires pour l'étudiant pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
2,642 notes
498 avis

À propos du cours

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....

Meilleurs avis

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 sur 489 Examens pour Apprentissage mechanique pratique

par 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.

par Surjya N P

Jul 03, 2017

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

par Sergio R

Sep 20, 2017

I miss Swirl

par 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.

par 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.

par 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.

par Raj V J

Jan 24, 2016

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

par 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.

par 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.

par 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.

par 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.

par 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.

par Henrique C A

Oct 14, 2016

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

par 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

par Serene S

Apr 29, 2016

too easy

par Baha`a A D

Oct 20, 2016

Good enough to open up mind of researcher

par Yury Z

Feb 03, 2016

I'm somewhat disappointed. I attend almost all other courses in this specialization (except of "data product") and this one is, on my opinion, the weakest one. A lot of links to useful information though. This is more reference guide rather than a real training course.

I can say even more, initially I start other courses of this specialization just because they were marked as strong prerequisite to this one. For now, I think all other courses of the specialization were much more valuable for me than this one.

I've also took Andrew Ng course on Machine Learning in the past, and my learning experience was much better. In lectures on some concepts (like regularization) I'm pretty sure I would not understand anything if I had not been familiar with the subject before..

par Yingnan X

Feb 11, 2016

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

par 王也

Dec 18, 2016

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

par CHEN X

Dec 03, 2015

Feels like everything is solved using a caret package, while the back-end theory is only slightly touched. By using a single line command solver, student may lack the foundation for harder problems in the real world.

par 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.

par 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.

par Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

par 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.

par Gulsevi B

Sep 23, 2016

Lectures are too complicated. I understand that material is not easy and one should do a lot of research and reading to understand the essence of the taught algorithms but the lecturer is also not very helpful and assignments are everywhere on the internet which nobody needs to get tired of thinking a little to do the homework as their product.