As soon as I discovered Plotly I rushed to stick it into just about every project I had at hand, and was almost immediately...annoyed. My introduction to visualization in python came primarily from matplotlib and it's stylish counterpart seaborn; trying to work with Plotly to create charts of the same stylistic caliber ended up turning into a bit of a time sink. Enter Plotly Express - the far more intuitive, easily styled implementation of Plotly I wanted.

I’m in the midst of unsteadily lurching through my first real Kaggle Competition, leaving behind the false comfort of the MNIST Digit recognition and other “Hello World” style machine learning problems. During this process it was very quickly made clear to me just how much of a beginner I am, and that, even with my background in philosophy, I’ve just barely scratched the surface when it comes to Natural Language Processing (NLP) in a Machine Learning context. This was made abundantly clear to me when, after looking at some of the leading kernels for inspiration, found they all practiced some form of NLP text preprocessing and analysis prior to feeding their data into the almighty Algorithm (typically BERT, in this scenario).