50+ days of Machine Learning

 Hello :) Today is Day 127! A quick summary of today: learned a bit more about Docker's potential and deployed a model to classify news text into categories ( github ) started watching Stanford's CS109 Probability for computer scientists  and the professor is so amazing saw a visualization of how activations change over epochs Firstly, about the mini-project After yesterday, I wanted to learn more about what Docker can do when it comes to MLops. Luckily, I found this tutorial  which develops a text's language classification model, bundles it with Docker and publishes an endpoint to Heroku. How did I do it? For a start, I wanted to do a simple model too, so I chose to write a simple model that classifies a piece of news into categories like sport, politics, tech, etc. I found a dataset on Kaggle and trained a simple multinomial naive Bayes that takes a tokenized bag of words and classifies them into sports, tech, politics, entertainment and business (the code is in my repo