Tharaka Hettihamu's repositories
my_portfolio
This a portfolio for myself by adding my details
ML-Assignment-CreditCardFraud
Credit card fraud datasets
PAFfoodies
Group project
free-programming-books
:books: Freely available programming books
machine-learning
:earth_americas: machine learning tutorials (mainly in Python3)
Random-Forest-Digits
A random forest digit classifier is a machine learning model that uses an ensemble of decision trees to classify handwritten digits. It is a highly accurate and widely used method for recognizing handwritten digits in image recognition tasks.
Gaussian-RBF-Descision-Boundary
In this project, we aim to explore the use of Gaussian RBF in creating a decision boundary for classification tasks using Python's scikit-learn library within a Jupyter notebook environment.
ai_all_resources
A curated list of Best Artificial Intelligence Resources
Iris-Logistic-Regression
Log regression
Pima-Diabetes-Predictor
This project is a Pima Diabetes Predictor, a machine learning model that uses data from the Pima Indian Diabetes dataset to predict whether a patient has diabetes or not.
Phone-Shop-Microservice
A microservices implementation for a phone company
Ocelot-Sample-Student-CRUD
Simple example of a CRUD (Create, Read, Update, Delete) application built using the Ocelot API Gateway framework. This project is intended to demonstrate how to use Ocelot to create a basic web API that can perform CRUD operations on a database of student information.
Linear-SVM-Classifier
In this project, we aim to explore the power of SVMs in classification tasks and demonstrate how to create a linear SVM classifier using Python's scikit-learn library within a Jupyter notebook environment.
machine_learning_basics
Plain python implementations of basic machine learning algorithms
profile
Config files for my GitHub profile.
react-car-app
Experimental Project for Scaffolding React website built with TailwindJs and NestJs API powered by MySQL
MADGroupProject
Everything related to the mobile app project
Oxford-Deep-NLP-Lectures
Oxford Deep NLP 2017 course