Logistic Regression from Scratch

During my journey as a Machine Learning (ML) practitioner, I found it has become ultimately easy for any human with limited knowledge on algorithms to take advantage of free python libraries such as scikit-learn to solve a ML problem. Truth be said, it's easy and sometimes no brainer to achieve this, as there are so many codes available in GitHub, Medium, Kaggle etc., You just need some amount of time looking at these codes to arrive at a solution to a problem of your choice. But, what if we learn every algorithm or procedures behind each machine learning pipeline that does all the heavy lifting for us inside these amazing libraries. In this blog post and the series of blog posts to come, I will be focusing on implementing machine learning algorithms from scratch using python and numpy. Sure you might argue with me for the first paragraph.