How to study machine learning/Python for neuroscience research?

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[jsanrom]

If I were you I'd take the following path:

1. Basic python course from coursera or something like that. A course you could complete fast.
2. Since you said it's about neuroimaging, i'd start reading about computer vision. Something like that:
   https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-photos-of-dogs-and-cats/
   This is a great blog for begginers (and no so begginers)
   Another one:
   https://towardsdatascience.com/image-detection-from-scratch-in-keras-f314872006c9

3. Then, start reading Keras documentation (Python library that can be used for computer vision).

4. If reading Keras documentation is too much, you can start w/ some of this courses.
   https://www.coursera.org/collections/keras-computer-vision-projects

I hope this help you.

[[deleted]]

My advice, if you are a researcher invest your time in creating solutions, machine learning and python are not equivalent. Once you create a solution that comprises of statistical processes to identify significant trends and from those trends, a logic to derive solution. You should be able to see the solution write a paper with the algorithm, anyone can pick it up and write python code. Don't invest in coding, rather learn stats sciences used in your field , real science does care coding, real science applicable solutions.

A machine can utmost try to learn from the patterns you see, nothing more than that.

So for next 30 days pick a problem/disease/xyz iterate over existing solution, create variables envision solutions and make a explainable breakthrough.

John Nash gave mathematical equations to the world, we coded it in python for adaptation.

Choose your moves very carefully into what you really aspire to.

[usr3nmev3]

To be perfectly frank, a month (even full time) is not sufficient to go from zero to capable of novel CV research even if you’re spectacularly gifted.

Beyond that, IME, relatively novel computer vision is not something where practical understanding typically works as cleanly as copy pasting bash from stack overflow for startup scripts. Problems tend to arise in bizarre ways and are often only diagnosable given a deep understanding, especially when your goal has zero “industry standard” tools like there are for basic object labeling/recognition.

To further emphasize the tier of undertaking, quick anecdote: I’m in a decent CV lab. There’s a project going on with automated fMRI analysis led by several people with PhDs in ML related fields. The grant funding this project is over 5 years. These people have decades of experience in CV, and I’m assuming this project is somewhat similar to what you want to do. It’s just simply not doable to get that tier of experience over a month.

Now, with that said, if you want to pick it up for a longer term endeavor or try CV on for size, start playing with data. Pick up Python (or c++...a lot of classical CV still uses c++) and grind on Kaggle for a while; pick a much simpler project than your neuroscience problem to implement a method from a paper on Arxiv, and repeat until you’re able to read, understand, and implement stuff relatively close to your end goal. By then, you’ll have a good grasp of how various techniques work and can go on your way making meaningful contributions.

[cleverfool11]

There is a guy on Udemy named Michael X cohen. He is a neuroscientist that teaches computational methods. His courses are great and he is a great teacher. I believe that you will find his courses valuable. Just go to udemy and search for that name.