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

4.5
2,637 notes
498 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.

DH

Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

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251 - 275 sur 489 Examens pour Apprentissage mechanique pratique

par Yong-Meng G

Jun 20, 2017

Insightful and practical ! One of the best so far.

par Laro N P

Jul 22, 2018

Good course, I miss more practice exercise because theory is always welcome but when we are capable to understand is doing real practice.

par Harland H

Oct 01, 2018

Very informative and the project was fun to accomplish.

par Bojan B

Oct 14, 2018

Great course with great materials. Easy to understand and to learn.

par asma m

Oct 02, 2018

The professor has a very clear lecture, brief and persistent comparing to others. I just love this course .

par Shashwat K

Oct 15, 2018

insightfull

par Peter B

Sep 06, 2018

Excellent lectures.

par Athanasios S

Aug 09, 2018

Great class! I wish you would do a little more explanation about what methods are best for which scenarios. If you did in fact explain that and it went over my head or I missed it, I apologize. Great class that I learned a lot from.

par Matthew S

May 08, 2019

Good introduction to machine learning. Provides pretty comprehensive coverage of major algorithms and approaches.

par Jerome S P

Jun 18, 2019

Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you

par Jeffrey M H

Jun 10, 2019

So far, one of the most fulfilling courses in the Data Science specialization!

par Andrew

Jul 24, 2019

Great intro to machine learning. Covers the basics to allow you to being using ML concepts on your own.

par Klever M

Jul 29, 2019

It was a great overview of the fascinating word of ML.

par Gustavo C G

Aug 07, 2019

Excellent introduction to machine learning. Great examples and detailed explanations, as usual

par Nino P

May 24, 2019

It's good that they teach you basics of machine learing in R (caret package), but it's very introductory course. I definetly recommend this course to beginner, but I also recommend taking more courses on this topic (Andrew Ng's for example).

par Martin G

Aug 13, 2019

Excellent course

par Deogratias K

Aug 16, 2019

I liked everything abt it

par Mary

Aug 19, 2019

Very informational with good variety of code to take back and apply to projects.

par Khalid S A

Aug 18, 2019

excellent course and very beneficial

par Andreas P

Aug 27, 2019

Thank you for helpful learning.

par John D M

Jul 15, 2019

A fast-paced course that got me going in building models and understanding the pitfalls. I felt the directions for the final project were somewhat poorly worded and vague (and calling one of the files test when it was not to be used for testing the model was initially confusing), but overall it was good. I would have liked to have seen the final project uploaded as a secure file as has been done in other courses, and Github was a poor platform for viewing html files. Additionally, the question about out of sample error caused many people problems in the projects as they confused it with with Accuracy, yet it was weighted heavily in the rubric: I'd like the instructors to review the materials how that material is presented in terms of models. I got 100%, but as always you have to pay very close attention to the rubric.

As always with this specialization, you are really just given a taste and there is no way you can fully explore all the material and references presented., but it is enough to get you going and wanting to come back and explore the material more.

par Umair R

Aug 29, 2019

Brian Caffo's courses are, as always brilliant.

par Charbel L

Sep 07, 2019

Excellent course. Shows how simple it is to start running models with machine learning...! Well done

par Muhammad Z H

Sep 15, 2019

learning alot

par YANAN D

May 27, 2019

elementary course and not too much work