Good

good

Good

good

COOL

good

good

good

good

Good

Nice

good

Good

GOOD

good

Nice

good

good

good

nil

A++

Ry

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g

The course aims to describe the key elements and techniques of machine learning but without delving into the associated maths. It manages to achieve this. The first lectures are delivered in a somewhat hesitant way but they improve over time and provide some very good insights as to how the procedures like convoluted neural networks work. Having only previously had exposure to Andrew Ng's Introduction to Machine Learning Course, I thought this course was a useful complement to the Stanford IMLC course.

So why not five stars? There were a few annoying things that could be improved IMHO:

1. The course is six weeks long, but there are only tests for the first four weeks. Once you complete these, you get messages saying you've completed the course, despite there being two (rather important) weeks left...why not have tests for the last two weeks of content??

2. The computing assignments are not graded, but there is no prior indication that this is the case! Probably just as well, since they assume a degree of familiarity with Python and Pytorch that is beyond the casual computer-literate person (though the Pytorch website provides lots of relevant help in this respect!) but it would have saved lots of angst to know that these were in effect optional.

3. The discussion boards are not reviewed by the course providers; numerous student questions are left unanswered, unless other students help. This is in sharp contrast to the Stanford course, where the discussion boards are very active and tutors are on hand to help and provide guidance.

But all in all, a useful and enjoyable course for a novice like me.