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I'm surprised that no one has yet mentioned Andrew Ng's Machine Learning course on Coursera and to go a bit deeper, his deep learning specialization on Coursera

I'm surprised that no one has yet mentioned Andrew Ng's Machine Learning course on Coursera and to go a bit deeper, his deep learning specialization on Coursera as well. Along with the programming assignments it's a solid way to get your feet wet. And definitely second the suggestion of the fast.ai courses as well

enraged_camel 9y ago
I took this course. Andrew is a great teacher. However, I wish he worked on his public speaking a bit. He has certain speech patterns, like starting sentences with "it turns out..." and after a while it becomes extremely irritating, at least to me. Fantastic course though.
- throwawaymanbot 9y ago
  wow... nitpick....
- enraged_camel 9y ago
  Lol, apparently we aren’t allowed to criticize people HN likes.
Okvivi 9y ago
As somebody that has started learning AI/ML and was looking at the exact question here three months ago - I found the Andrew Ng deeplearning.ai Coursera course an amazing starting point. It was high level enough, and got me to understand enough, to get me to a point where I could start trying to build a side-project, without exposing me to the deeper math behind Neural Nets. It was a great starting point without being overwhelming. Now I feel that I have the option to either go deeper if I need to, or go wider. I find Andrew Ng to be an amazing teacher - explains things simply, clearly, and in a way that I find super easy to understand.
- pj_mukh 9y ago
  On that note, do any of these courses take a deep dive on the deeper mathematics of neural nets?
  - tom_wilde 9y ago
    Yes, see: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning It starts out reaaaally basic but give a thorough grounding of the maths and the intuition behind it.
makmanalp 9y ago
I think fast.ai is the more "programmer-y" do-first-learn-as-you-need approach, and Andrew Ng's is the more "math-y" learn-basics-work-your-way-up approach, and they can work well together too.
- tinymollusk 9y ago
  This is true, but it's more than that: the fastai professors (Jeremy + Rachel) have studied how people learn. How teachers can structure courses to maximize understanding. They believe (and have research backing them up), that the way we teach math (base and rote concepts, building until you can understand something complex) is sub-optimal. They dive into the code and get stuff done, then later bubble back up for concepts. For me, it was bewildering at first, but if you can trust your instructor, you trust they won't leave you stranded. (It does also require the type of student who does a lot of study on their own!)
cr0sh 9y ago
I took this course in the Fall of 2011, before Coursera existed, when it was introduced to the public as "ML Class" (sponsored by Stanford). I agree - it's a great introduction, and I learned a ton from it (and it answered a ton of questions I had until then).
vaibkv 9y ago
Does not matter the number of courses one does. Knowing when to apply what and practical know-how only comes when you do real world projects on your own. In OP's case, it might very well be building NLP projects, doing image classification (insects and plants, maybe?), making one's own recommender system to suggest movies - but unlike Netflix this will also recommend dissimilar ones. Writing or improving on research papers is another way to go about it.

enraged_camel 9y ago

I took this course. Andrew is a great teacher. However, I wish he worked on his public speaking a bit. He has certain speech patterns, like starting sentences with "it turns out..." and after a while it becomes extremely irritating, at least to me. Fantastic course though.

throwawaymanbot 9y ago
wow... nitpick....
enraged_camel 9y ago
Lol, apparently we aren’t allowed to criticize people HN likes.

Okvivi 9y ago

As somebody that has started learning AI/ML and was looking at the exact question here three months ago - I found the Andrew Ng deeplearning.ai Coursera course an amazing starting point. It was high level enough, and got me to understand enough, to get me to a point where I could start trying to build a side-project, without exposing me to the deeper math behind Neural Nets. It was a great starting point without being overwhelming. Now I feel that I have the option to either go deeper if I need to, or go wider. I find Andrew Ng to be an amazing teacher - explains things simply, clearly, and in a way that I find super easy to understand.

pj_mukh 9y ago
On that note, do any of these courses take a deep dive on the deeper mathematics of neural nets?
- tom_wilde 9y ago
  Yes, see: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning It starts out reaaaally basic but give a thorough grounding of the maths and the intuition behind it.

makmanalp 9y ago

I think fast.ai is the more "programmer-y" do-first-learn-as-you-need approach, and Andrew Ng's is the more "math-y" learn-basics-work-your-way-up approach, and they can work well together too.

tinymollusk 9y ago
This is true, but it's more than that: the fastai professors (Jeremy + Rachel) have studied how people learn. How teachers can structure courses to maximize understanding. They believe (and have research backing them up), that the way we teach math (base and rote concepts, building until you can understand something complex) is sub-optimal. They dive into the code and get stuff done, then later bubble back up for concepts. For me, it was bewildering at first, but if you can trust your instructor, you trust they won't leave you stranded. (It does also require the type of student who does a lot of study on their own!)

cr0sh 9y ago

I took this course in the Fall of 2011, before Coursera existed, when it was introduced to the public as "ML Class" (sponsored by Stanford). I agree - it's a great introduction, and I learned a ton from it (and it answered a ton of questions I had until then).

vaibkv 9y ago

Does not matter the number of courses one does. Knowing when to apply what and practical know-how only comes when you do real world projects on your own. In OP's case, it might very well be building NLP projects, doing image classification (insects and plants, maybe?), making one's own recommender system to suggest movies - but unlike Netflix this will also recommend dissimilar ones. Writing or improving on research papers is another way to go about it.