A Detailed Guide on Optimization and Stochastic Gradient Descent — The aim of this article is to establish a proper understanding of what exactly “optimizing” a Machine Learning algorithm means. Further, we’ll have a look at the gradient-based class (Gradient Descent, Stochastic Gradient Descent, etc.) of optimization algorithms. NOTE: For the sake of simplicity and better understanding, we‘ll restrict the…

A Complete Guide to the KNN Classification Algorithm, where We Will See How to Implement a KNN-Based Machine Learning Model from Scratch, while Understanding the Mathematics Behind it. — When it comes to solving classification problems via machine learning, there’s a wide variety of algorithm choices available for almost any data type or niche problem that one might be dealing with. These algorithmic choices can be broadly categorized into two groups, which are as follows.

Your Complete Guide to Multinomial Logistic Regression, a.k.a. Softmax Regression — When it comes to real-world machine learning, around 70% of the problems are classification-based, where, on the basis of the available set of features, your model tries to predict that out of a given set of categories(discrete possible outcomes), what category does your target variable might belong to. Today, in…

A Complete Guide on How to Code a Polynomial Regression Model & the Math Behind It — When it comes to predictive analysis, regression models prove to be one of the most cost-efficient methods. While a linear regression model can provide some good predictions, in several cases the polynomial regression models can vastly outperform simple linear models. …

Your Complete Guide to Linear Regression — In this project, we will see how to create a machine learning model that uses the Multiple Linear Regression algorithm. The main focus of this project is to explain how linear regression works, and how you can code a linear regression model from scratch using the awesome NumPy module. Of…