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

2,642 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


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.


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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376 - 400 sur 489 Examens pour Apprentissage mechanique pratique

par Yew C C

Feb 04, 2016

Wish to have more systematic structure, detail information and hands-on exercises.

par Daniel U

Feb 17, 2016

Fast paced and little focused on the algorithms but quite useful overall.

par Yukai Z

Dec 09, 2015

A good introductory course for people who has an interest in knowing the principles of machine learning and want to make a step forward. Sufficient details covered throughout the course and additional resources were provided which are very useful. Quizzes were well designed with minor improvements in the accidental mismatch of the answers due to package version issues. Overall the study experience was enjoyable and would definitely recommend to someone who wants to start knowing data science.

par danxu

Mar 14, 2017

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

par Bruce I K

Oct 20, 2016

It's a great course but I hope you add a few things. The course about the machine learning algorithm is so basic. Please get deep into the machine learning algorithm. Then it would become the perfect course.

par carlos j m r

Oct 05, 2017

I thought there were Swirl practice as other courses, however this course is very good.

par Erika G

Jul 28, 2016

I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.

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 Brandon K

Mar 30, 2016

The lectures were great and engaging. I felt like they went too fast. Jeff says at the beginning that this is just an overview and points to some other resources. As an overview, this class works well. You can expect to learn a bit about what machine learning is and how to to do it using the caret package in R.

par Robert K

Nov 14, 2017

I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course! Still, much benefit and many useful concepts covered which can be revisited in greater detail down the track.

I would also like to see the final assignment change subtly every so often as there are existing completions on the web and it's too easy/tempting for some to simply copy and paste.

par Daniel R

May 14, 2016

The course is really great, however it should last a little longer, 4 weeks is hard to accomplish

par Chonlatit P

Oct 20, 2018

GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.

par Jakub W

Sep 24, 2018

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

par Johnny C

Oct 23, 2018

It was in general nice course. However, quizzes need improvement.

par Samuel Q

Oct 24, 2018

Good course to get only the basics of machine learning. The assignments and quizzes are great but the lecture material is very brief and short. The references provided throughout the lectures are probably the best source of more information.

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 Jiarui Q

Mar 27, 2019

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

par Sanket P

May 27, 2019


par Erik K

Jul 08, 2019

Very good. Learned a lot

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 Robert S

Sep 16, 2019

The lecture material is great, but the quiz material is in need of updating. R and it's packages have gone through many updates since the problems were written so it is sometimes difficult to reproduce their results even with running the sample codes given after getting the answer correct.

par Daniel J R

Jan 17, 2019

Seems like a lot to pack into 4 -weeks. Should really be named introductory machine learning. Needs more depth and better development of the intuitions associated to each algorithm class to match the expectations.

par Raul M

Feb 12, 2019

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

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.