Chevron Left
Retour à Apprentissage mechanique pratique

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

3,107 évaluations
589 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.

Filtrer par :

451 - 475 sur 579 Avis pour Apprentissage mechanique pratique

par Vincent G

22 oct. 2017

appropriately challenging material.


7 oct. 2016

Nice Course for every New candidate

par Tiziano V

25 mai 2017

Interesting the final assignment.

par Rahul K

7 mars 2016

Really Well Structured Course!!

par Robert R

20 juil. 2016

Just the right level of detail

par Erik K

8 juil. 2019

Very good. Learned a lot

par Bassey O

3 mai 2016

Very informative course.

par Qian W

9 sept. 2018

need eva on my project

par Javier R

14 oct. 2017

Love this class !

par Mehul P

3 oct. 2017

Good ML overview.

par Lilia K R E

30 mars 2016

Muy buen curso :)

par Tiberiu D O

21 sept. 2017

A good course!

par RAO U D K

17 sept. 2020

Excellent job

par Raymond M

2 mai 2018

pretty good!

par Piyush P

13 juil. 2017

good context

par Prahlad S

18 juin 2020

great hands

par Timothy V B

22 avr. 2017

good course

par Rohit K S

21 sept. 2020

Nice One!!

par Ryan R S

25 août 2020

Very Fun


19 avr. 2018


par Sanket P

27 mai 2019


par Yury Z

3 févr. 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 June K

5 sept. 2019

This course does not have the depth it needs, but I do learn a few valuable things. I suggest breaking this course into 2 courses and give more lectures on using caret package and other packages as well. Another thing is I could not ever find the correct answers for the quizzes, and most of the time has to guess and take the quizzes 3 times to get things right.

I invested time and effort in doing the last project; but got a not so good grade due to peer review process. I got every requirement done and even have a direct link to my HTML final report but 2 out of my 4 my peer reviewers have limited knowledge of GitHub could not find my link to HTML file. That said with a higher level courses, peer review process has to be different.

par Francois v W

10 déc. 2017

The course gives a decent overview of the model building process and covers a good spread of machine learning methodologies. I found that the videos focused too much on some basic/immaterial concepts at times and tended to gloss over the more in-depth or complicated sections. It would have helped if difficult concepts were explained with more examples. This meant that a lot of self study outside the lecture notes had to be done. The way that the final assignment had to be submitted on Github resulted in me spending 8 times longer on learning how to post my results than actually building the model - some more guidance here would have helped a lot as the process was very frustrating.

par Dheeraj A

17 janv. 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.