good

good

good

...

10.

amz

Top

Top

wow

no

ok

很好

:)

很好

ok

<3

5

好

-

♥

m

.

v

It's fine if you have a relatively strong background in implementing "multi-step" mathematics in Python. I would not say this is for an actual beginner. Maybe not even someone who is concurrently learning Python AND this course at the same time. I'm rating the course highly for the subject matter that it presents, but I struggled IMMENSELY during the practicals (you actually code key portions of the definitions--but not the entire Jupyter notebook).

The video portion is awesome. Andrew ("Dr. Ng"?) provides an excellent "plain-english" down-to-earth explanation of the math behind the algorithms. The code, however... Well, let's just say it FEELS like one of those art-instruction jokes: "Drawing an Owl: First you start with two circles. Second, you draw the rest of the (explicative) owl."

I don't really think I have much of a furture in anything remotely involving math and programming. After this course, I honestly feel more inclined to stick to dumping data with SQL and letting the grownups slice and dice it.

Bottom line: I've been in IT for over two decades and have alot of (outdated) skills in my toolbag, but this course brought me to tears of feeling like my brain is finally starting to slip away. I just can't learn stuff the way I used to. I don't know. Maybe this will all make more sense after I've slept on it.

Great introductory course, Andrew is really talented in making everything he says crystal clear. However, I've found few minor things I don't like:

1. As someone who started this course when it was free, I can say that previous version offered much more insights and tougher assignments and harder quiz questions, it was harder overall. This version feels kinda dumbed down a bit.

2. Some (important?) things are left unrevealed, not enough attention is paid to the issue of feature selection and feature engineering (maybe some of it will be covered to extent in other specialization courses). That last assignment that included regularization in logistic regression had already given function which mapped 2d features into 27 dimensions, and someone without much math background could not really see how to map such cases by themselves. Maybe that stuff is out of scope of this course, but whole model fails if someone doesn't know how to do that input preprocessing and knowledge about algorithm then becomes irrelevant.

That being said, whole course was amazing and interactive, with really valuable content, especially for a beginner.

4.5/5 from me