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

4.5
2,675 notes
501 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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301 - 325 sur 492 Examens pour Apprentissage mechanique pratique

par Nilrey J D C

Dec 01, 2017

Good introduction to machine learning

par Lukas M

Oct 06, 2017

The lectures are very good to get the basic knowledge about machine learning. One suggestion is that the lectures can be longer, covering more detailed stuff and a little bit more advanced materials. Moreover, some codes are not explained clean and clear for me. Hope it would be better in the future.

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 Ramiro A

Aug 31, 2016

Nice course, Gives a god insight on what can me done with R and Predictions

par Jason M C

Mar 29, 2016

Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable. I really liked how the class was more in the style of 'here's some techniques, now do whatever you want on the project.' Prior courses are, and understandably so, more constrained in the assignments. It's not until here that the student really has the tools to be able to flex their analytical muscles, and it pays off.

Also, of the three instructors, I am most favorable to Jeff Leek, who teaches this class. He communicates much clearer than Roger Peng or Brian Caffo. I find I learn more from his content than the others.

Lastly, I will say that this class doesn't hold a torch to University of Washington's Machine Learning specialization. That's expected since this is one class and that's a whole series of classes. If you're hungry for more after this one, I highly recommend UWash's Machine Learning specialization.

par Rahul K

Mar 07, 2016

Really Well Structured Course!!

par Chris M

Aug 14, 2016

Unlike the rest of the modules in this specialisation, this one was well taught, a good blend of theory and practice and well paced.

There were still a few issues with wording in quizzes (and some where there seemed to be two identical answers to one question, where one would be considered right and the other wrong - purely chance). In addition, the lack of consistency in how to submit assignments across the specialisation is frustrating, I'm not sure if it's supposed to be a way to show how to use github or something like that, but it shouldn't be the case.

par bhawani p

Jan 07, 2017

briefly summarised the machine learning algorithms. Good place to start!

par Rohit P

Nov 13, 2016

Lectures were not very detailed.

Quizzes were good and challenging, but too many times the results didn't match the answers even when the random seed was set right

Final project should have been more challenging with more models to build and compare

par Craig S

Feb 12, 2018

Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

par Robert R

Jul 20, 2016

Just the right level of detail

par Jeffrey E T

Mar 28, 2016

Good overview of available techniques and the Caret package. Will get you started in machine learning.

par Steve d P

Mar 20, 2016

Nice, other courses will go more in depth though.

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 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 Guilherme C

May 18, 2016

Title says everything. Practically and basically no theory explained. Good course though.

par Rui W

Aug 27, 2017

know some packages of machine learning using R

par Marcus S S

Feb 25, 2017

Great course! The hands-on approach make it very useful for one to start doing some very interesting analysis in real life! Thanks a lot! You guys could only make some efforts in updating some classes and packages used in quizzes. But the rest was great!

par Alfredo M

Aug 22, 2016

Excelente curso. Ótimo conhecimento dos instrutores.