I'm about to finish the ML course (after 3 tries.) And essentially you have to battle the huge amount of boredom and keep doing it week after week without stop. Just yesterday I took a quiz without even watching the lecture or anything: scored 4.5/5. Proceeded to finish the programming assignment, again without bothering with the lecture, 100/100. Not to say I won't watch the lecture later, I want to make sure I know exactly the way it's done (will do today probably, I wanted to finish the assignments fast this week because last I almost passed the deadline.) But they are passable without that much effort. Just persistence.
But they are passable without that much effort. Just persistence.
It depends on a person's background. There are people like Jeff Heaton who have years of professional experience in Data Science taking an introductory data science course, and there are invariably people taking machine learning with no programming experience. But in the sweet spot for any class there will be people who are really stretched. People who will spend twenty or thirty hours on a programming assignment that some people complete in one.
I enrolled in the last iteration of Van Hentenreck's Discreet Optimization. A great course and his enthusiasm is infectious. I learned a lot. Saw where I needed to go. But there was no chance I was going to pass. I just don't have the chops...yet, hopefully.
One of the things that makes Discreet Optimization a great MOOC course is that it can be approached at different levels. A student can attack the problems using dedicated optimization libraries. If that's not enough of a challenge, they can write their own algorithms. And if that's not enough, they can prove optimality for each of their solutions.
And like every Coursera computing course there are people who can do all of it. And most who cannot.
Damn, now I had to add it to my watch list in Coursera, seems very interesting. Of course, my background is in maths, so even if I did no machine learning back then I know how to fill the gaps as needed and as fast as possible. If someone just rang my door and told me he passed ML without any prior programming experience, I'd hire him: it definitely shows an incredible will and self-learning ability. In my case... Well, I got to know a little better some formalisms of machine learning, and also got me thinking more about ML problems, which is fun and gets me brewing with interesting ideas.
Discreet Optimization is full of fertile ideas. And they're all NP hard. And that means that any success is meaningful. It inspired me to work on getting the tools I need for another assault.
I've become good at understanding lecture videos played back at faster than real-time. Sometimes I can get up to 2x playback speed with VLC and still understand what's being said. It'd be so boring watching things in real-time. I'm so glad Coursera let you download videos so you can do that.
Yes, absolutely have to agree with this! The lectures can be time consuming in some of the classes and this really makes it bearable. You really just need to get the gist of what the professor is presenting and then use the lecture notes/presentations as-needed. I would probably skip the video lectures altogether, but occasionally there is something in the lectures themselves that you need to pick up on. At least this has been my experience with the "Data Analysis" classes, my first foray into MOOCs.
The online viewer at Coursera (including the iOS app) has also speed setting: I watch them at between 1.5 and 1.75. Occasionally 2x, and more often than not I skip 30 sec chunks when nothing interesting is going on.
Yes X 1000! Once I started doing this I started to finish
courses, and lectures. Before, I was nodding out within
a half hour. I'm not a smart guy either. Just an average,
or low average IQ(too lazy to take the test actually). I
watch the videos at 1.4x or higher, and slow down the speed
when something complex is introduced. (Teacher must have
good diction, and an accent I'm familiar with--no mumblers)
