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

4.5
2,667 notes
500 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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451 - 475 sur 491 Examens pour Apprentissage mechanique pratique

par Jorge B S

Jun 25, 2019

I have passed 5 courses of this specialization and I am not fully satisfied with this one. The course is a very brief introduction to practical machine learning, as the concepts are explained very fast and without a minimum level of detail. Then, most importantly, there are no swirl exercises, so it is quite difficult to put the acquired knowledge into practice. The other 4 courses I took, they all had swirl and that was great. Nevertheless, the course project is quite nice in order to face a real machine learning problem.

par Manuel E

Aug 08, 2019

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

par Rok B

Aug 08, 2019

The material is well choosem but poorly explained. This course among all would need swirl excercises, or just more excercises in any form. Instead the lecturer rushes through the material. So in the end you do have some overview about machine learning in R but not enough hands on experie

par Davin G

Aug 26, 2019

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

par June K

Sep 05, 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 Yohan A H

Sep 06, 2019

I think it was a very fast course and I feel more real examples would have been useful,

par Philip E W J

Jan 30, 2019

Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.

par Alexander R

Aug 21, 2017

Very basic, might as well just read a cheat sheet. No explanation of how or why to choose different options in a pipeline, for example, which data slicing to use (k-folds, bootstrap, etc). Just runs through how to do them.

par Pawel D

Jan 22, 2017

This course is rather bad, not well rehearsed and hastily delivered. Especially in comparison with other, in-depth course of this Specialization. The course is more of a 'caret' package review then actual Machine Learning. I learned how to use the

par Michael S

Feb 07, 2016

Had big expectations for this one... really one of the ones to look forward to after working through the beginning of the specialization, but for some reason, it seemed any prof or even TA interaction was absent this time around like in none of the other specialization coursed to date. Bugs in the new interface and quizzes weren't really addressed. Couldn't even get an official response about the apparent removal of Distinction-level now (which I'd been working to get in all specialization courses and now seems no longer an option). Still interesting content. As a "free" course, it's still really valuable. As one of the people that paid for this and all others in this specialization, this is the one I felt didn't return as much value to justify the payment with no "official" course staff seeming to be involved this round.

par Damon G

Mar 02, 2016

The mathematics in this course are at a high level (similar to Statistical Inference) - and are presented at a pace that is challenging without significant background in the field. There is little guidance presented on the methods required. It is recommended that students source out plenty of support material (intro to statistical inference and similar).

par ANDREW L

Jan 27, 2016

Does not give much intuition around the subject. I found the lectures a bit uninspiring. Lots of powerpoint (just text, no images or visuals really) and the lecturer just underlined the words he was talking about as he read the powerpoint out. I found the Udacity Intro to Machine Learning course gave a much better intuition and understanding of this subject. We also had slides on how to split data into a training and testing set on pretty much EVERY lecture - what a waste of time!

par Tim A

Oct 14, 2016

This is a part of the data specialization; from afar, I would not be interested in Machine Learning because of this course. I will seek other methods to learn.

par Arcenis R

Feb 26, 2016

The instructions for the final project were very unclear and even though I submitted all assignments well before their respective deadlines and reviewed the required number of projects my work was not processed for a grade thereby delaying my specialization completion.

par Marshall M

Sep 23, 2017

A lot of the concepts in the course are grazed over very briefly and don't go into that much depth. In addition, some of the concepts are taught as concepts, they are taught through examples which tends to contextualize the material. Good content but could be put together in a more in depth manner.

par Felipe M S J

Dec 02, 2016

No es un curso en el que se aprenda demasiado.

Parece demasiado avanzado en el uso de "caret" y en vez de enseñar, parece ser que todo debe ser aprendido con anterioridad.

Todo el material adicional que se necesita en el curso, es en general contenido externo.

par max

Jan 18, 2017

not what I expected for a machine learning course

par Jonathan O

Apr 18, 2016

I saw two main issues with this course: 1) dated lecture videos, oftentimes with R code that can't be replicated using up-to-date packages, and 2) lack of thoughtful design: example after example after example after example doesn't really teach you anything.

par Vincenc P

Mar 31, 2016

Course content feels upside down. You'll learn about machine algorithm specifics and caveats before anyone explains what the said algorithm actually hopes to achieve.

par Jeffrey G

Sep 12, 2017

Course project was the only project work, needed more. This course should also use swirl(). Quizzes et al contained mistakes.

par Mehrshad E

Mar 28, 2018

This course really lack something like SWIRL. The lectures only provide a summary, which is not helpful for someone new to the machine learning. Also, the instructure tries to cover pretty much everything but not in depth; instead, I think fewer topics should be covered in depth.

par Anju M

Apr 17, 2016

Felt difficult in understanding the overall course in short duration . 1 month is not enough for this course. I request the authors to make the course much more simpler

par Yang L

Aug 14, 2016

needs more case studies and examples

par Allister G A

Dec 25, 2017

The course needs to elaborate more on hands on discussions.

par Stefan K

Mar 10, 2017

Very shallow content - broad, but not deep. Not many assignments instead of the last one. We hear what we heard before. For the same price, Analytics Edge at EdX is far better choice for practical machine learning.