Comentários e feedback de alunos de Aprendizagem Automática na Prática da instituição Universidade Johns Hopkins

Awesome Course !!

Very interesting course

I enjoyed working on this course. There is a lot good and

Excellent course for beginner.

No swirl exercises, but last project totally worth my time.

Nice course.

very clear explanation

Very fine course in machine learning where the focus is more on the use of ML rather on the theory behind it i.e. the course title fits its contents.

It is exactly what you need to begin in datascience.

Very good client's service if you've some problem.

ok

Good

Good course about Machine Learning in R.

Excellent course, very practical !

I am very curious about the maths so I will add some specialized certifications

Excellent course.

Jeff Leek is a great professor .The delivery of the course material is very clear and covers a lot of predictive methods by using mainly R's caret package. Recommended for sure.

intense one :-)

Excelente curso!

Good practice. Very practical skills learned

In general, great course. But because of the strong interest in ML, I am going to attempt a detailed review.

PROS:

This course truly de-mystifies "Machine Learning". After completing the course, you will be able to programmatically use 100s of ML algorithms that have been created by others over the years. You will be able to use the Caret package in R to simplify your application, simplify pre-processing, perform automatic cross-validation/model tuning and generate various statistics about the model used by your ML algorithm. You will be able to easily estimate out-of-sample accuracy to determine if your model has any hope of working well, picking one classifier over the other, or using several classifiers to estimate outcome. You will learn how some of the heavily used algorithms in the industry work behind the scenes, and where to go to learn more about these. Several learning databases are introduced. If you tinker with them, you will be amazed at how easy R and Caret make it to apply ML algorithms. You will understand how chatbots, recommender systems, spam filters, "prediction" systems and the like work.

WHAT THIS COURSE DOES NOT COVER:

It does not cover how to write your own ML algorithms. That requires working knowledge of optimization algorithms, advanced math and probably lots of other resources.

WHO SHOULD TAKE THIS COURSE?

Only those prepared to work hard, dig in, and persevere through a lot of (sometimes difficult) material will benefit from this course. If you're not confident about your statistics concepts, not comfortable with R and databases, not comfortable with googling for parameters and techniques not directly discussed in class slides, then you will have trouble. Passing the quizes require you to refer to material from prior weeks, read online documents and look for similar solutions at stackexchange etc.

TIP FOR MENTORS:

For every week of the course, create a pinned post which says "Tips/Errata for Quiz #n". You've collected sufficient feedback from students now and know what the common issues are. Don't make them search through 100s of discussions to figure out solutions to well-known/common problems.

very good and informational

Excellent course

Awesome!

Great course. Ways to curb plagiarism & cheating needs to be revisited by your team.

The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.

I give 5 stars because at the end I was able to understand the different topics and obtained a deeper sense of what can be done with the tools provided, however I believe there should be one section to have a complete exercise in order to better understand how to organize a project and put together all the tools provided. Perhaps a general diagram for analysis may be good to have as well.