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I have completed my PhD few months back. I am about to start my postdoc at my PhD lab soon. I will be working on a different project than my PhD.

My PhD was in computational materials science. I used open-source finite element software to carry out my research. I had to write Matlab scripts for developing 3D geometries (used a bit of computational geometry: voronoi and a bit of topology optimization) and python scripts for postprocessing of the simulation results. The codes I wrote were quite basic.

Now, for the postdoc position, I have to implement machine learning algorithms in my research. I have 6 months before I need to provide tangible results. I need to learn and be proficient in python modules.

How to go ahead with learning the language proficiently and also learn the fundamentals of machine learning in 6 months. Is it possible to do so? Or I am tackling an impossible task? Any guidance with the course material?

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  • Of course it is possible. Many people have done it successfully. Also, many people have failed. My advice: Study some documentation and tutorials. Then participate on Stack Overflow. I did that in 2011 and after half a year I was a decent R programmer. – Roland Oct 15 '20 at 6:07
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Yes

Barring exceptional circumstances, this should be possible. It depends, of course, on a few things:

  1. How much time you have (e.g. hours per week that you can spend on studying)
  2. What resources you have at your disposal (some courses are for free, others are not, and of course you need the appropriate hardware)
  3. What is meant by "tangible results"
  4. How fast you learn

There are many courses that teach machine learning in python. Here is a list of some courses, most have a duration of 3-6 weeks, with 3-8 hours per week. This blog entry lists free courses that focus on specific python libraries that are useful for machine learning. I checked the contents of a few, and they consisted of a series of video lectures, with only a couple of hours in total length.

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