The 20th century turned out to be an era of exponential growth in the field of machine learning. The 3000-year-old ancient game of'Go' that computer scientists predicted will take another decade to crack was made possible by Google Brain teams AlphaGo AI, defeating multiple-time world champion Lee Sudol. And, by the way, this Chinese game has more combinations than predicted atoms in the universe or, in short, this game can't be won just by running through all the possible moves, what IBM Blue did in 1997, defeating world champion Gary Kasparov. Research communities are thriving in ML, from 100 papers submitted annually 10 years ago, to 100 per day in 2019 on arXiv alone. But, keeping everything aside, the point is that ML is highly math-intensive. While libraries like TensorFlow and PyTorch have made a significant contribution in making ML reachable to all the developers out there, we still have a steep learning curve to know how to create models, train them, and save it to later use it for our tasks.