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

3,121 évaluations
593 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

13 août 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

28 févr. 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 sur 583 Avis pour Apprentissage mechanique pratique

par Gabriela C V

14 déc. 2020

It's harder than the previous one. it would be nice to update some the quizzes as they are based on older versions of R Studio libraies.

par Hernan S

13 déc. 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

24 sept. 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

14 août 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

10 oct. 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

27 sept. 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

10 janv. 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

8 févr. 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

2 mars 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

19 nov. 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

20 oct. 2017

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

par marcelo G

14 août 2016

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

par Jeffrey E T

28 mars 2016

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


24 juil. 2017

This was a superb module which created a deep learning insight within me focusing on future technology

par João R

20 août 2017

Got confused how to perform cross validation and when. Other than that, very practical. Great job.

par Daniel R

14 mai 2016

The course is really great, however it should last a little longer, 4 weeks is hard to accomplish

par César A C

26 juil. 2018

Very interesting course. May be a little bit harder than the previous ones but it could be done.

par Greig R

13 nov. 2017

Good course, I learnt a lot. It does need to be updated with more modern versions of software.

par Pieter v d V

28 juin 2018

Very quick overview. If you really want to know something about it read the reference books.

par Guilherme C C

18 mai 2016

Title says everything. Practically and basically no theory explained. Good course though.

par Carlos C

12 août 2017

Excellent content so I give 4 starts. I stat less because the trainer speaks too fast.

par carlos j m r

5 oct. 2017

I thought there were Swirl practice as other courses, however this course is very good.

par alon c

10 mars 2016

Great Course, will be nice to have more projects to see how it goes with different data

par Anant S

30 juin 2017

good course for initial understanding of machine learning. SVM can also be included.

par Caio H

23 août 2019

I learned a lot in this course, but I would recommend taking the courses in order.