-
A Brief Introduction to Linear Discriminant Analysis
Learn about Linear Discriminant Analysis (LDA) and its applications in classification and dimensionality reduction. Discover how it can help extract features.
Sunil Kumar 14 Oct, 2024
-
Understanding Linear Regression with Mathematical Insights!
Linear regression is supervised learning algorithm. In this article we will understand the Linear regression with mathematical insights.
Harsh 17 Aug, 2021
-
Mathematical Modelling: Modelling the Spread of Diseases with SIRD Model
In this article, we will explore Mathematical Modelling. We will Modelling the Spread of Diseases with SIRD Model and deriving insights
Ilekura 10 Aug, 2021
-
Logistic Regression in Machine Learning
Master Logistic Regression in Machine Learning with this comprehensive guide covering types, cost function, maximum likelihood estimation, and gradient descent techniques.
Anshul 25 Apr, 2025
-
Understanding The What and Why of Gradient Descent
Gradient descent is an optimization algorithm used to optimize neural networks and many other machine learning algorithms.
Rajdeep 30 Jul, 2021
-
How much Mathematics do you need to know for Machine Learning?
Mathematics is a subject that is required in almost any field. In this article, we will look into the Mathematics For Machine Learning.
KAVITA 31 Mar, 2023
-
A Simple Guide to Hypothesis Testing for Dummies!
Hypothesis testing quantifies an observation or outcome of an experiment under a given assumption and interpret whether it holds or not
Vennela 28 Jul, 2021
-
Mathematics Behind Principle Component Analysis In Statistics
This article is based on the mathematics behind the dimensionality reduction techniques called Principle Component Analysis
Mayur 28 Feb, 2025
-
Get Started with Statistics for Data Science
Statistics is the study of collecting and extracting information from quantitative data for making inferences and decisions
Akil 22 Jul, 2021
-
Absolute Beginner’s Series To Implement Neural Networks(Part-1)
Neural networks has become a buzzword these days because of their ability to do an amazing amount of work in various domains.
Adwait 22 Jul, 2021
