Chevron Left
Retour à Apprentissage mechanique pratique

Avis et commentaires pour d'étudiants pour Apprentissage mechanique pratique par Université Johns-Hopkins

2,894 évaluations
548 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


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.


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.

Filtrer par :

451 - 475 sur 540 Avis pour Apprentissage mechanique pratique

par Gulsevi R

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.

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 Rok B

Aug 08, 2019

The material is well choosem but poorly explained. This course among all would need swirl excercises, or just more excercises in any form. Instead the lecturer rushes through the material. So in the end you do have some overview about machine learning in R but not enough hands on experie

par 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.

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 Eric S

Jul 05, 2020

Weakest class in the Data Science Specialization so far. Don't expect to leave with a deep understanding of the machine learning techniques covered in this course. You will get practice using the caret package in R, which is very useful.

par 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 :-)

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 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 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.

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 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

par 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.

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 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.

par 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.

par 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.

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 Davin G

Aug 26, 2019

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

par 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.

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 Max M

Dec 12, 2017

Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.

par 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.

par Manuel E

Aug 08, 2019

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

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.