Today I was fiddling around with setting up python on Visual Studio Code a very elegant text editor that is well integrated with various programming languages. I had been using it for a while for editing bash code, and after exploring a few IDE options, I decided to just stick with it. Today I spent some time integrating python with VS code, and going through a machine learning exercise in python.
One note that I will make is that it makes sense to work with a virtual environment (I prefer conda), so that we can install python packages without disrupting the computer base code. Here and Here and Here are useful links with dealing with conda virtual environment and python.
Getting Started with Python Extension in VS Code: https://code.visualstudio.com/docs/python/python-tutorial
Working with Jupyter Notebooks: https://code.visualstudio.com/docs/python/jupyter-support
Tutorials:
Setting up virtual environment: https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/
Machine Learning Tutorial: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
Keras Tutorial: https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
As a side note, sci-kit seems like a generally useful package for implementing machine learning, and pandas seems to. be particularly useful for working with data frames. They even have a conversion table for R users!
Sci-Kit: https://scikit-learn.org/stable/
Pandas: https://pandas.pydata.org/
Pandas conversion table for R: https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_r.html#compare-with-r
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