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

4.5
étoiles
2,850 évaluations
540 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

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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351 - 375 sur 531 Avis pour Apprentissage mechanique pratique

par Aashaya M

May 29, 2016

In my opinion this course is highly technical and demanding in nature compared with the others. The learning experience is good and coursera.org has given a opportunity for customization ! thank you Coursera

par Paul K

Apr 08, 2017

Very good summary of strengths/weaknesses of various machine learning algorithms. This lecturer's style and production quality is much higher than in the previous two courses in the specialization series.

par 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.

par 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.

par Matthew C

Dec 11, 2017

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

par Utkarsh Y

Nov 17, 2016

Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.

par Craig S

Feb 12, 2018

Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

par Roberto G

May 21, 2017

Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice

par Eric L

Jun 02, 2016

Great course, very high paced with a lot of information. would have been great to add two more weeks and another project to use more machine learning

par Igor H

Sep 10, 2016

Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.

par Lee G

Sep 22, 2017

A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.

par Yashaswi P

May 24, 2020

Good Course the covers a lot of practical aspects and relevant to the real world solution.

Good References and Learning Materails are available

par Ann B

Sep 06, 2017

Good class to get the basics of Practical Machine Learning. This course is best taken as a part of the data science series from John Hopkins.

par Hernan S

Dec 13, 2016

The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.

par 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

par 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.

par Rhys T

Oct 10, 2017

Good course, some aspects of the assignment were a bit beyond the scope of what the course teaches but overall I learnt a lot.

par Níck F

Sep 27, 2016

Was pretty good, but quite short and some assignments did not align as well with the lecture material as they could have.

par Michael O D

Jan 10, 2020

This is a great course, but it would be good to see it updated to use the newer evolution of the caret package, parsnip.

par Tongesai K

Feb 08, 2016

Very good course. I am very knew to this topic but am sure will find a lot of application in my speciality - geophysics

par Kevin S

Mar 03, 2016

Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.

par Sulan L

Nov 19, 2018

I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.

par A. R C

Oct 20, 2017

I enjoyed it but it needs indeed to deep into many concepts, which are just briefly named during the course.

par marcelo G

Aug 15, 2016

Great course, very demanding, but it could use more reading material, ebooks instead of links on video.

par Jeffrey E T

Mar 28, 2016

Good overview of available techniques and the Caret package. Will get you started in machine learning.