Chevron Left
Retour à Apprentissage mechanique pratique

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

4.5
2,610 notes
492 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.

AS

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

Filtrer par :

301 - 325 sur 483 Examens pour Apprentissage mechanique pratique

par Javier R P

Oct 14, 2017

Love this class !

par marcelo G

Aug 15, 2016

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

par Kalle H

Jun 25, 2018

Nice course that tries to fit a lot of material into four weeks. Due to this, the material is not so deep, although pointers are given to where the student can find additional information related to each subject covered by the course.

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 Tiberiu D O

Sep 22, 2017

A good course!

par Minki J

Dec 29, 2017

good to know many concepts of machine learning model.

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 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 KRISHNA R N

Apr 19, 2018

nice

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 Saurabh K

Mar 09, 2017

Very useful course to develop level knowledge in machine learning.

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 Alia E

Jul 13, 2018

Really could have used a few more examples.

par Nguyen T T

Apr 20, 2018

Thank you! My teacher. The course very good. Many thanks

par Tiziano V

May 25, 2017

Interesting the final assignment.

par Andrew K

Mar 13, 2017

So why four stars vs five stars, of all the Data Science Certification courses that I have taken: i) some of the examples and quiz challenges don't work as they should, ii) Machine Learning is rapidly changing area - should be updated to reflect this and perhaps a high level taste of Deep Learning, iii) posting the Final Project is overly complicated relative to methods of the other courses - this should be cleaned up - still not clear how point to a github repo link and also have a rendered html page working from that same link - requires two links to present materials and must use default names like index vs. a project name.

par Ramiro A

Aug 31, 2016

Nice course, Gives a god insight on what can me done with R and Predictions

par Jason M C

Mar 29, 2016

Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable. I really liked how the class was more in the style of 'here's some techniques, now do whatever you want on the project.' Prior courses are, and understandably so, more constrained in the assignments. It's not until here that the student really has the tools to be able to flex their analytical muscles, and it pays off.

Also, of the three instructors, I am most favorable to Jeff Leek, who teaches this class. He communicates much clearer than Roger Peng or Brian Caffo. I find I learn more from his content than the others.

Lastly, I will say that this class doesn't hold a torch to University of Washington's Machine Learning specialization. That's expected since this is one class and that's a whole series of classes. If you're hungry for more after this one, I highly recommend UWash's Machine Learning specialization.

par Guilherme C

May 18, 2016

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

par Rui W

Aug 27, 2017

know some packages of machine learning using R

par Alfredo M

Aug 22, 2016

Excelente curso. Ótimo conhecimento dos instrutores.

par Marcus S S

Feb 25, 2017

Great course! The hands-on approach make it very useful for one to start doing some very interesting analysis in real life! Thanks a lot! You guys could only make some efforts in updating some classes and packages used in quizzes. But the rest was great!

par Robert W S

Nov 22, 2016

Great intro to machine learning. Several algorithms with some ideas on sampling and pre-processing techniques are covered. Adding a textbook as done with some of the other data science classes would help, but other resources are referenced.

par Piyush P

Jul 13, 2017

good context

par Pieter v d V

Jun 28, 2018

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