7 ms·

This is one of the best courses on ML.

by farzatv 4y ago

smnrchrds 4y ago
What are the others? Any recommendations?
- _odey 4y ago
  Not a full course I'd say, but I've used this one to learn the math behind deep neural networks and code my own from scratch in elixir and C: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/
  - lagrange77 4y ago
    Yes, this is really good! Andrew Ng, too.
  - vasili111 4y ago
    What is required math for starting Ng course?
    - _odey 4y ago
      Can't tell you about Andrew Ng's coruse as I haven't done that, but for Michael Nielsen's course it was Matrices and Partial Derivatives. I'm assuming it's quite similar.
- UmbertoNoEco 4y ago
  Depends, how much linear algebra, probability and python do you know?
  - samstave 4y ago
    What if one knows zilch, like my teenager... What might be best to start the path?
    - UmbertoNoEco 4y ago
      OK, since it is for a teenager I would assume just basic computer competency (how to install programs and stuff like that) but nothing else, so apologies if some of these things are pretty obvious/basic.. I would (assuming zilch knowledge and tools). - Install Python. https://www.python.org/downloads/ https://www.python.org/downloads/ - Install VSC https://code.visualstudio.com/ https://code.visualstudio.com/ - Install the python extension for VSC https://marketplace.visualstudio.com/items?itemName=ms-python.python https://marketplace.visualstudio.com/items?itemName=ms-pytho... - learn a little bit of python. This is a good resource, but there are several more, even the official documentation is very good http://openbookproject.net/thinkcs/python/english3e/ http://openbookproject.net/thinkcs/python/english3e/ - After some familiarity with python one option is this free book: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ - Another, good (but paid) option is this book: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646 https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T... Or a third option is indeed Andrew Ng's course. Now, for any of those 3 options a little (or a lot) of guidance and patience will be needed so hopefully you or some friend/peer can help with that. Good luck!
- rripken 4y ago
  I took these courses from Georgia Tech via OMSCS but they are also on udacity. https://omscs.gatech.edu/cs-7641-machine-learning https://omscs.gatech.edu/cs-7641-machine-learning https://omscs.gatech.edu/cs-7642-reinforcement-learning https://omscs.gatech.edu/cs-7642-reinforcement-learning (I took this before ML but its supposed to come after. There is some overlap. Probably my favorite graduate course.) https://omscs.gatech.edu/cs-7646-machine-learning-trading https://omscs.gatech.edu/cs-7646-machine-learning-trading (IMO not amazing) Much more basic (took this before OMSCS): https://www.udacity.com/course/intro-to-machine-learning--ud120 https://www.udacity.com/course/intro-to-machine-learning--ud... I'm sure there are many more.
  - deskamess 4y ago
    I found ML4T to be really accessible and fun. Cannot say the same for ML.
  - jay3ss 4y ago
    What about machine learning for trading didn't you like?
- nicd 4y ago
  I highly recommend https://course.fast.ai/ https://course.fast.ai/. It's much more top down: in the first lesson or two, you train a NN image classifier, rather than starting with first principles and linear algebra. I found this structure to be more motivating and effective.
  - woah 4y ago
    Fast AI teaches you a little bit more about being a practitioner, dealing with datasets, pointing the right algorithms at the right data and checking whether you get good results. Andrew Ng for me did a lot more to demystify how stuff actually works
  - saynay 4y ago
    I went through both, but I definitely think fastai is the better starting point.
    - bmitc 4y ago
      Can you say more?
      - jpgvm 4y ago
        I didn't do these particular courses but I found it a lot easier to stay motivated with the top down approach. First demonstrate usefulness, then deepen fundamentals. When I was younger and didn't work full time + have other commitments the bottoms up approach appealed to me more, I think partially because I had bigger time blocks to allocate. i.e I could spend a whole weekend just learning fundamentals of some particular thing I was interested in and reach the first levels of usefulness in that one "session". These days smaller time blocks mean that I need to walk away with something the keep the spark going for most curiosities.
        Simon_O_Rourke 4y ago
        > When I was younger and didn't work full time + have other commitments I second this - while both are great courses, I found I could only dedicate very short amounts of time recently to any kind of study, and going from the ground-up more thoroughly seemed like I was making no progress. The fast.ai top down approach worked a bit better for me for those reasons, otherwise it would have been interesting starting with the deep dive.
- ForHackernews 4y ago
  "Learning from Data" is outstanding: https://work.caltech.edu/telecourse.html https://work.caltech.edu/telecourse.html It's a recorded version of a real Caltech undergrad course, and it's focused on understanding the math behind these algorithms, not just applying black-box ML libraries. It's much less practical, but I feel like it teaches you more.

This is one of the best courses on ML.

smnrchrds 4y ago

What are the others? Any recommendations?

_odey 4y ago
Not a full course I'd say, but I've used this one to learn the math behind deep neural networks and code my own from scratch in elixir and C: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/
- lagrange77 4y ago
  Yes, this is really good! Andrew Ng, too.
- vasili111 4y ago
  What is required math for starting Ng course?
  - _odey 4y ago
    Can't tell you about Andrew Ng's coruse as I haven't done that, but for Michael Nielsen's course it was Matrices and Partial Derivatives. I'm assuming it's quite similar.
UmbertoNoEco 4y ago
Depends, how much linear algebra, probability and python do you know?
- samstave 4y ago
  What if one knows zilch, like my teenager... What might be best to start the path?
  - UmbertoNoEco 4y ago
    OK, since it is for a teenager I would assume just basic computer competency (how to install programs and stuff like that) but nothing else, so apologies if some of these things are pretty obvious/basic.. I would (assuming zilch knowledge and tools). - Install Python. https://www.python.org/downloads/ https://www.python.org/downloads/ - Install VSC https://code.visualstudio.com/ https://code.visualstudio.com/ - Install the python extension for VSC https://marketplace.visualstudio.com/items?itemName=ms-python.python https://marketplace.visualstudio.com/items?itemName=ms-pytho... - learn a little bit of python. This is a good resource, but there are several more, even the official documentation is very good http://openbookproject.net/thinkcs/python/english3e/ http://openbookproject.net/thinkcs/python/english3e/ - After some familiarity with python one option is this free book: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ - Another, good (but paid) option is this book: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646 https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T... Or a third option is indeed Andrew Ng's course. Now, for any of those 3 options a little (or a lot) of guidance and patience will be needed so hopefully you or some friend/peer can help with that. Good luck!
rripken 4y ago
I took these courses from Georgia Tech via OMSCS but they are also on udacity. https://omscs.gatech.edu/cs-7641-machine-learning https://omscs.gatech.edu/cs-7641-machine-learning https://omscs.gatech.edu/cs-7642-reinforcement-learning https://omscs.gatech.edu/cs-7642-reinforcement-learning (I took this before ML but its supposed to come after. There is some overlap. Probably my favorite graduate course.) https://omscs.gatech.edu/cs-7646-machine-learning-trading https://omscs.gatech.edu/cs-7646-machine-learning-trading (IMO not amazing) Much more basic (took this before OMSCS): https://www.udacity.com/course/intro-to-machine-learning--ud120 https://www.udacity.com/course/intro-to-machine-learning--ud... I'm sure there are many more.
- deskamess 4y ago
  I found ML4T to be really accessible and fun. Cannot say the same for ML.
- jay3ss 4y ago
  What about machine learning for trading didn't you like?
nicd 4y ago
I highly recommend https://course.fast.ai/ https://course.fast.ai/. It's much more top down: in the first lesson or two, you train a NN image classifier, rather than starting with first principles and linear algebra. I found this structure to be more motivating and effective.
- woah 4y ago
  Fast AI teaches you a little bit more about being a practitioner, dealing with datasets, pointing the right algorithms at the right data and checking whether you get good results. Andrew Ng for me did a lot more to demystify how stuff actually works
- saynay 4y ago
  I went through both, but I definitely think fastai is the better starting point.
  - bmitc 4y ago
    Can you say more?
    - jpgvm 4y ago
      I didn't do these particular courses but I found it a lot easier to stay motivated with the top down approach. First demonstrate usefulness, then deepen fundamentals. When I was younger and didn't work full time + have other commitments the bottoms up approach appealed to me more, I think partially because I had bigger time blocks to allocate. i.e I could spend a whole weekend just learning fundamentals of some particular thing I was interested in and reach the first levels of usefulness in that one "session". These days smaller time blocks mean that I need to walk away with something the keep the spark going for most curiosities.
      - Simon_O_Rourke 4y ago
        > When I was younger and didn't work full time + have other commitments I second this - while both are great courses, I found I could only dedicate very short amounts of time recently to any kind of study, and going from the ground-up more thoroughly seemed like I was making no progress. The fast.ai top down approach worked a bit better for me for those reasons, otherwise it would have been interesting starting with the deep dive.
ForHackernews 4y ago
"Learning from Data" is outstanding: https://work.caltech.edu/telecourse.html https://work.caltech.edu/telecourse.html It's a recorded version of a real Caltech undergrad course, and it's focused on understanding the math behind these algorithms, not just applying black-box ML libraries. It's much less practical, but I feel like it teaches you more.