An Essential Guide to Numpy for Machine Learning in Python

Well since most of us tend to forget(In case of those already who already implemented ML algorithms) the various library functions and end up writing code for pre-existing functions using sheer logic which is a waste of both time and energy, in such times it becomes essential if one understands the nuances of the Library being used efficiently. So Numpy being one of the essential libraries for Machine Learning requires an article of its own. Since understanding Numpy is the starting point of Data Pre-processing and later on implementing ML Algorithms, So you can be someone who is about to learn Machine Learning in the near future or has just begun and wants to get a more Hands on experience in learning Numpy for ML. But my main focus while writing this article is for it to serve as a quick refresher to Numpy for those who have had experience with the library but need a swift recap. Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover Numpy forms the foundation of the Machine Learning stack.