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

4.5
2,677 notes
501 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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376 - 400 sur 493 Examens pour Apprentissage mechanique pratique

par Coral P

Aug 19, 2017

The project is good in letting us practise what we learnt

par Subrata S

Mar 09, 2017

Very good course. The content can be enriched with some more technical details behind the various techniques. There needs to be 1 more course on Practical Machine Learning in the specialization as 1 course is far too less for such a vast topic.

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 Mehul P

Oct 03, 2017

Good ML overview.

par Stephan H

Aug 12, 2017

Very challenging course. I learned a lot. Tanks.

par Sean Q Z

Dec 11, 2016

As the title states, very practical way to show you how this is done in R.

Most of them are lines of codes and some explanation. There are tons of details behind that and remains un-explained.

As other courses in the specialization, students need to do a lot of self-study to further understand machine learning.

But at least, learned a lot.

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 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 danxu

Mar 14, 2017

very good, but if it has swirl practice like th other courses it would be perfect.

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 Nilrey J D C

Dec 01, 2017

Good introduction to machine learning

par Lukas M

Oct 06, 2017

The lectures are very good to get the basic knowledge about machine learning. One suggestion is that the lectures can be longer, covering more detailed stuff and a little bit more advanced materials. Moreover, some codes are not explained clean and clear for me. Hope it would be better in the future.

par Carlos M

Jul 12, 2017

A good course, but the field is so large and so important. You'll really need the "hacker" mentality to get through this course. They DO NOT teach you even close to everything you'll need to complete the course. It's also very stats/math heavy which will make the theory difficult. This isn't why I only rated 4 stars. I did so because of the lack of Swirl and the feeling that I still don't feel like I understand the topic well enough to do anything in a business setting yet. I was hoping for more from the class.

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 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 Timothy V B

Apr 22, 2017

good course

par Javier R P

Oct 14, 2017

Love this class !

par Jorge E M O

Sep 07, 2018

The course rushes over a lot of concepts and it already shows its age - however, it's a pretty solid introduction to machine learning from a practical perspective. It will provide you with a lot of ideas for further investigation and exploration and in the end you'll end up with a wide vision of the machine learning process.

par César A

Jul 26, 2018

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

par Sanket P

May 27, 2019

ok

par Oliver S

Jul 26, 2019

A reference solution for the quiz questions as there are in some other courses in this specialization would have been nice, since I got sometimes very different results using the newest versions of the libraries and I'd really like to know, if I made any big mistakes and it's not only because of my setup.

par Caio H

Aug 23, 2019

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

par Erik K

Jul 08, 2019

Very good. Learned a lot