ARAVIND G's Projects
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Asp.Net Admin Panel Open Source Project
All-in-one stop for all my notes in various DevOps topics.
amplication-demo
This repository is used for Angular Development
CSS Fundamentals: From Basics to Advanced
HTML Fundamentals: From Basics to Advanced
This repository contains my mini projects on any programming languages I am currently learning.
NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed. This tutorial explains the basics of NumPy such as its architecture and environment. It also discusses the various
Python tutorial: start here if you don't know Programming
A beginner-friendly tutorial for learning Python programming from scratch. Covers basic concepts with practical examples and exercises. Suitable for students, hobbyists, and aspiring programmers. Join us and let's learn Python together!
C programming tutorials I enjoy making
:closed_book: Both personal and public notes for EC-Council's CEHv10 312-50, because it's thousands of pages/slides of boredom, and a braindump to many
Exploratory Data Analysis on the Chocolate Bar Ratings dataset provided on Kaggle by Rachael Tatman
👨⚕️ A fully featured Clinic Management System based on three tier architecture made using ASP.NET, C# with a well documented README.md file.
Course Files for Complete Python 3 Bootcamp Course on Udemy
Basic to Advance Complete Python Tutorials
Contains Basic Python tutorials for beginners
The Data Engineering Cookbook
CRUD Operation in react.js and mysql
Used twitter api to parse crypto tags as part of the research.
An ongoing collection of awesome ethical hacking tools, software, libraries, learning tutorials, frameworks, academic and practical resources
Projects and awesome list for all Data Science fields
Machine learning techniques. Supervised: linear and logistic regression, classification tree (decision tree), random forest and neural network. Unsupervised: k means cluster, hierarchical clustering and principal component analysis (pca).