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

4.5
étoiles
2,978 évaluations
568 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

MR

Aug 14, 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

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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501 - 525 sur 559 Avis pour Apprentissage mechanique pratique

par Raj V J

Jan 24, 2016

more needs to be taught in class. what is taught is not sufficient for quizzes.

par Surjya N P

Jul 03, 2017

Overally course is good. But weekly programming assignments will be great.

par 王也

Dec 18, 2016

Too different for beginners but not deep enough for ones already know R.

par james

Sep 10, 2016

Quizzes are useful exercises but need to do a lot of self studying.

par Philip A

Feb 27, 2017

mentorship was great, but the video lectures were almost useless.

par Christoph G

Dec 04, 2016

The topic is too big, for one course from my point of view.

par Ariel S G

Jun 27, 2017

In my opinion, this course needs a few extra exercises.

par Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

par Bahaa A

Oct 20, 2016

Good enough to open up mind of researcher

par Johnnery A

Mar 20, 2020

I need study more this course

par Sergio R

Sep 20, 2017

I miss Swirl

par Serene S

Apr 29, 2016

too easy

par Estrella P

Jul 07, 2020

.

par Miguel C

May 10, 2020

I really enjoyed the content of the course. I already knew a fair amount about machine learning but I learned a lot more than I thought I would. Most contents of weeks 3 and 4 - decision trees and random forests, bagging and boosting, linear discriminant analysis and naive Bayes, forecasting and unsupervised predictions - were my favourite topics in this course.

The biggest disappointment in this course for me were the outdated quizzes. I worked really hard through this course and most of the Data Science specialisation. But the quizzes are set up for older versions of R and some of its packages, so the results are completely different from what I got most of the time. I found this extremely frustrating and disheartening and had to repeat the quizzes several times. I do realise that most quizzes enumerate at the beginning the versions they are using, but there is no mention of how one goes about to set that up in R. On top of that, given that I rarely passed the quiz on the first try my Skill Tracking score dropped considerably, undermining weeks and weeks of hard work.

Unfortunately, this tainted my view of this course and I would advise the course organisers to update it as soon as possible.

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 Agatha L

Jan 23, 2018

I was disappointed with this course. For better or worse ML is a part of data science and, in this course, the instructional depth was lacking. The lectures provided examples of how to implement a few ML algorithms in R, with very little actual instruction on the intricacies of these algorithms, theoretical foundations etc. Taking the course I felt somewhat cheated (a google search would have done the job of the class), and frustrated with various little bugs in Quiz/Assignment content.

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 Fulvio B

May 24, 2020

This course is not at the same level of the other courses I followed in the data science specialization. The lessons seem easy but when confronted with practicalities you realise you are missing practical tools. Moreover, sometimes the code is not up to date with a package and some datasets not available anymore. This creates problems with the quizzes since sometimes is not possible to reproduce one of the given options. I do not think this is acceptable for these kind of courses.

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 Leo C

Jul 16, 2020

This course is getting too old. Some assignments are impossible to do since modern implementation of packages used are getting a COMPLETELY different answer. The theory is ok, if a bit all over the place, but it's extremely frustrating believing you did something wrong just cause your answers are better than the answers the quizes believe they should be.

par Peter G

Feb 28, 2016

Absolutely useless random un-explained list of facts and advices that is thrown to a learner without any attempt to give a systematic approach. Pure waste of time and effort. Can only be suitable to those, who already know the subject well and can use some additional facts that are randomly presented in this "course".

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 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 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 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.