Polynomial regression bridges the gap between simplicity and complexity in modeling relationships. Let me walk you through the nuances of polynomial regression, sharing what I've learned along the way. What is Polynomial Regression? At its core, poly...
In the realm of machine learning and statistics, regression metrics are crucial for evaluating how well our models perform. When we build a regression model, our primary goal is to make accurate predictions. To assess the effectiveness of these predi...
Hey there! Welcome to the journey of understanding the l algorithm of machine learning most. Whether you’re a student, a budding data scientist, or just someone curious about how we can make sense of data, this blog is for you. Linear regression is o...
Hi there, in this blog we’ll be exploring about Principal Component Analysis (PCA). It is one of the most useful techniques in MLOPs that helps us make sense of complex data by reducing its dimensions while retaining essential information. If you've ...
Frame The Problem. Framing the problem is the crucial and very first step in `MLops`. Problem can be of type - prediction, classification, clustering etc. Deciding and framing the problem helps us to choose algorithms among pool of available option t...
Simply put - “Tensors are containers for numbers.” Yes, it’s a fancy name of data structure. A tensor is a mathematical object that generalizes scalars, vectors, and matrices to higher dimensions. It can be of 0 dimensional, 1 dimensional, 2 dimensio...
