5 ms·

Coursera ML course wasn't particularly hard

by caub 10y ago

Nimitz14 10y ago
I'm guessing the author hasn't a maths education beyond high school.
- mrborgen 10y ago
  I have a bachelors degree in economics, so some university math with calculus and linear algebra. The reason I found it hard was because I had to learn Octave while also understanding the algorithms and general ml concepts. That being said, I think it's a great course, and would highly recommend it to anyone who thinks they're up for it. But there are other courses that provides an easier gateway into ML.
  - truth_sentinell 10y ago
    Like which one?
    - mrborgen 10y ago
      Udacity's Intro to Machine Learning
      - deleted 10y ago
        [deleted]
      - rrherr 10y ago
        For another option, similar to the Udacity course, I recommend this: http://www.dataschool.io/machine-learning-with-scikit-learn/ http://www.dataschool.io/machine-learning-with-scikit-learn/
  - sndean 10y ago
    > The reason I found it hard was because I had to learn Octave while also understanding the algorithms and general ml concepts. I had the same experience, even after using Matlab some in grad school. I wish I had seen this before trying Ng's course (all of the exercises in Python): http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ http://www.johnwittenauer.net/machine-learning-exercises-in-... So much quicker / easier to follow, at least for me.
    - caub 10y ago
      scipy is almost exactly the same than matlab/octave
      - p1esk 10y ago
        You shouldn't need scipy for this. Perhaps you meant 'numpy'?
pzh 10y ago
Assuming you're taking about Andrew Ng's Stanford ML course, I had the same impression until I realized that the actual Stanford ML course is a lot harder and theory-heavy. Basically, the Coursera thing is a more practical, watered-down (less math) version of the real Stanford course (which btw you can still find on Youtube, though it may be a bit dated)...
yodsanklai 10y ago
If you already know partial derivatives, gradient descent algorithms, and some linear algebra, it's easy. The assignments are not challenging either, even compared to other coursera classes. But for people that don't have this background, it can be hard. I think the problem with Coursera is that they try to accommodate as many students as possible, which means the classes aren't very deep. I don't think this should be the role of a ML class to teach about partial derivatives or matrix factorization for instance. It's great that the class is accessible to many people, but sometimes you feel that you just scratch the surface, and that you're not at the level of university students that went through more challenging classes. As a comparison, I did the labs of 6.828 and 6.824 (OS and distributed systems) on MIT opencourseware and it was much harder and much more rewarding than any coursera class. And I did only the labs, there is much more material to cover to do it seriously (quizzes, lot of articles to read...). Kudos to MIT students! (I wonder how many such classes they take during one semester). Too bad opencourseware classes aren't always properly set up for outside students. There may be some material missing, no forum to ask questions, video quality can be very bad and so on...
larve 10y ago
Here is the stanford cs229 course, which is a proper deal more in depth: https://see.stanford.edu/Course/CS229 https://see.stanford.edu/Course/CS229
- dfan 10y ago
  Yeah, when I did the Coursera course I supplemented it with the CS229 materials to get more of the theory behind everything, which worked great. If you want more math rather than less, it's the way to go.
- rawnlq 10y ago
  I found the related course listing from that link really useful: https://see.stanford.edu/materials/aimlcs229/AI-classes.pdf https://see.stanford.edu/materials/aimlcs229/AI-classes.pdf But there seem to be a few famous ones (such as cs231n) missing. Any current stanford students want to chime in on what else is hot nowadays? In addition what's a recommended course sequence that will take you from cs229 to a bleeding edge deep learning expert?

Coursera ML course wasn't particularly hard

by caub 10y ago

Coursera ML course wasn't particularly hard

Nimitz14 10y ago

I'm guessing the author hasn't a maths education beyond high school.

mrborgen 10y ago
I have a bachelors degree in economics, so some university math with calculus and linear algebra. The reason I found it hard was because I had to learn Octave while also understanding the algorithms and general ml concepts. That being said, I think it's a great course, and would highly recommend it to anyone who thinks they're up for it. But there are other courses that provides an easier gateway into ML.
- truth_sentinell 10y ago
  Like which one?
  - mrborgen 10y ago
    Udacity's Intro to Machine Learning
    - deleted 10y ago
      [deleted]
    - rrherr 10y ago
      For another option, similar to the Udacity course, I recommend this: http://www.dataschool.io/machine-learning-with-scikit-learn/ http://www.dataschool.io/machine-learning-with-scikit-learn/
- sndean 10y ago
  > The reason I found it hard was because I had to learn Octave while also understanding the algorithms and general ml concepts. I had the same experience, even after using Matlab some in grad school. I wish I had seen this before trying Ng's course (all of the exercises in Python): http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ http://www.johnwittenauer.net/machine-learning-exercises-in-... So much quicker / easier to follow, at least for me.
  - caub 10y ago
    scipy is almost exactly the same than matlab/octave
    - p1esk 10y ago
      You shouldn't need scipy for this. Perhaps you meant 'numpy'?

pzh 10y ago

Assuming you're taking about Andrew Ng's Stanford ML course, I had the same impression until I realized that the actual Stanford ML course is a lot harder and theory-heavy. Basically, the Coursera thing is a more practical, watered-down (less math) version of the real Stanford course (which btw you can still find on Youtube, though it may be a bit dated)...

yodsanklai 10y ago

If you already know partial derivatives, gradient descent algorithms, and some linear algebra, it's easy. The assignments are not challenging either, even compared to other coursera classes. But for people that don't have this background, it can be hard. I think the problem with Coursera is that they try to accommodate as many students as possible, which means the classes aren't very deep. I don't think this should be the role of a ML class to teach about partial derivatives or matrix factorization for instance. It's great that the class is accessible to many people, but sometimes you feel that you just scratch the surface, and that you're not at the level of university students that went through more challenging classes. As a comparison, I did the labs of 6.828 and 6.824 (OS and distributed systems) on MIT opencourseware and it was much harder and much more rewarding than any coursera class. And I did only the labs, there is much more material to cover to do it seriously (quizzes, lot of articles to read...). Kudos to MIT students! (I wonder how many such classes they take during one semester). Too bad opencourseware classes aren't always properly set up for outside students. There may be some material missing, no forum to ask questions, video quality can be very bad and so on...

larve 10y ago

Here is the stanford cs229 course, which is a proper deal more in depth: https://see.stanford.edu/Course/CS229 https://see.stanford.edu/Course/CS229

dfan 10y ago
Yeah, when I did the Coursera course I supplemented it with the CS229 materials to get more of the theory behind everything, which worked great. If you want more math rather than less, it's the way to go.
rawnlq 10y ago
I found the related course listing from that link really useful: https://see.stanford.edu/materials/aimlcs229/AI-classes.pdf https://see.stanford.edu/materials/aimlcs229/AI-classes.pdf But there seem to be a few famous ones (such as cs231n) missing. Any current stanford students want to chime in on what else is hot nowadays? In addition what's a recommended course sequence that will take you from cs229 to a bleeding edge deep learning expert?