How to Build an End-to-End Deep Learning Portfolio Project

It was in the late December 2020 when one evening, I was casually scrolling through my Twitter timeline that I caught a tweet from a famous YouTuber that I followed and I paused. He had tweeted about how it was a pain to go through the huge number of comments that each of this videos received and how too often, so many good comments -- to which he would've really loved to reply to -- get lost in the sheer volume. Being a data science practitioner, I was intrigued by the idea of efficiently handling such a huge inflow of comments on videos. Upon thinking about it for a few hours, I was ready to believe that it really was a genuine problem. It was then that the idea of doing a project based on that particular use case was born.