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

4.5
étoiles
2,769 évaluations
519 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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401 - 425 sur 510 Avis pour Apprentissage mechanique pratique

par Rahul K

Mar 07, 2016

Really Well Structured Course!!

par Robert R

Jul 20, 2016

Just the right level of detail

par Erik K

Jul 08, 2019

Very good. Learned a lot

par Bassey O

May 03, 2016

Very informative course.

par Qian W

Sep 09, 2018

need eva on my project

par Javier R P

Oct 14, 2017

Love this class !

par Mehul P

Oct 03, 2017

Good ML overview.

par Lilia K R E

Mar 30, 2016

Muy buen curso :)

par Tiberiu D O

Sep 22, 2017

A good course!

par Raymond M

May 02, 2018

pretty good!

par Piyush P

Jul 13, 2017

good context

par Timothy V B

Apr 22, 2017

good course

par KRISHNA R N

Apr 19, 2018

nice

par Sanket P

May 27, 2019

ok

par Christian B

Apr 30, 2017

First I want to thank very much the instructor in the online forum. He helped me a lot at the end of the course and his tutorials for gh-pages are excellent. He was also very fast in responding. Thank you.

The course did ultimately not really gave me what I was looking for. Maybe too may different facts and not enough depth. I am not sure that I can confidently say that I can build a ML model now. Technically I can, but the deeper understanding is missing. For example: When would I use which method (for example rf versus naive base), the last exercise about cross validation was not fully clear. Using the caret package is too high level for a learner. It would be better to see some more step by step examples. It was not clear to me what the expected error calculation in the last exercise was really looking at. Maybe what is missing a swirl exercises, not using caret. and then explaining how caret can simplify it. We also learned how to create a predictive model, but did not go into how the model gets updated and gets retrained, an important aspect of ML. i also do not see unsupervised learning to be covered.

par Yury Z

Feb 03, 2016

I'm somewhat disappointed. I attend almost all other courses in this specialization (except of "data product") and this one is, on my opinion, the weakest one. A lot of links to useful information though. This is more reference guide rather than a real training course.

I can say even more, initially I start other courses of this specialization just because they were marked as strong prerequisite to this one. For now, I think all other courses of the specialization were much more valuable for me than this one.

I've also took Andrew Ng course on Machine Learning in the past, and my learning experience was much better. In lectures on some concepts (like regularization) I'm pretty sure I would not understand anything if I had not been familiar with the subject before..

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 Francois v W

Dec 10, 2017

The course gives a decent overview of the model building process and covers a good spread of machine learning methodologies. I found that the videos focused too much on some basic/immaterial concepts at times and tended to gloss over the more in-depth or complicated sections. It would have helped if difficult concepts were explained with more examples. This meant that a lot of self study outside the lecture notes had to be done. The way that the final assignment had to be submitted on Github resulted in me spending 8 times longer on learning how to post my results than actually building the model - some more guidance here would have helped a lot as the process was very frustrating.

par Dheeraj A

Jan 18, 2016

I believe this course is critical and much needed given where the Industry is heading. Prof Leek, has tried his best to explain the concepts in a lucid manner, however the complexity of the content, may challenge most students.

A few more examples with R code would have been helpful as translating problem statement to R code may not be intuitive.

I would highly recommend that students should plan to study some advance statistics before attempting this course. Having said that, i think this is a wonderful starter course to get a glimpse of what Machine Learning is all about.

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 Samy S

Apr 23, 2016

As as standalone course on machine learning, it's probably best to take Andrew Ng's class on Coursera. This course mostly teaches the basic usage of the caret package. It is too short to cover more fundamental topics in machine learning, like how to choose an algorithm based on the problem and the data.

I took this class just because I was engaged in the Data Science specialization. I wanted to clear the Capstone project and get the Data Science specialization certificate.

par Paul R

Mar 13, 2019

A key course everything has been building towards, some important concepts and modeling techniques are introduced. However Jeff rushes through a lot of material, and I think this would be better served as two courses with more case studies and exercises, especially as the capstone doesn't use much of this. But nevertheless a useful introduction to this topic, concepts of training vs. testing etc, different models to be used, along with the caret package in R.

par Eduardo P

Apr 14, 2017

This is such a cornerstone topic to the Data Science Specialization that I think it deserves a better designed and more polished curriculum. The subject is so extensive that it might be worthy to split the contents in two courses. Finally, I would like to suggest the authors of the course modeling the curriculum following the amazing treatment of the subject found in "Introduction to Statistical Learning" by Hastie, Tibshiriani et. al.

par Ehsan K

May 30, 2019

This is a good course for someone who has already done the previous courses in this specialization series.

It covers the most basic ideas in machine learning and expose you to work on real problems and learn by experience. if you are looking for more advanced in-depth courses, you need to take other courses as well.

Overall, lectures are in very fast pace and as a result they have several mistakes in them you should be careful about.

par Rafael M

Mar 30, 2016

The course feels rushed. I understand teaching Machine Learning in 4 weeks is impossible, but then maybe the course needs to have a narrower yet deeper scope rather than throw at you many concepts without details. e.g. trees, random forests, bagging and boosting all in 10 minutes each? Impossible...

So, as opposed to creating machine learning intuition I feel the course became an R package code book.