ML lab programs done as part of college coursework

pytorch and basic Ml algorithms implementation both in R as well as Python with ready to use datasets

Basic data visualizations, Statistics and Machine Learning algorithms are implemented using R Language

Basic data visualizations, Statistics and Machine Learning algorithms are implemented using R Language

collection of some interesting personal projects. Afaratlas , Amharic-NLP,General-NLP,Generative-Learning-algorithms,RL

Data Science project about Social Media using supervised, unsupervised and reinforcement Machine Learning algorithms.

A Bidirectional LSTM model to classify whether a given tweet talks about a real disaster or not. This was my project in "CSC 522: Automated Learning and Data Analysis" course at NC State University.

Coding various ML Algorithms from scratch

Hey 👋 , Made Road-Map for ML ( Machine 🤖 Learning ) & DL ( Deep 🤖 Learning ) Learner .

Machine learning algorithms from ML course, Coursera, University of Michigan

Some final reports from my senior-level case studies course (STA440 at Duke University - Fall 2019)

This repository contains custom implementation of ML algorithms from scratch.

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

This is a second version of my previous work

Machine Learning Concepts And Models using Octave and Jupyter Notebook

This repository consists of files required to deploy a **Machine Learning** Web App created with **Flask**

Collection of my projects which involve many machine learning algorithms.

Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.Decis…

This project demonstrates the implementation of a DC GAN, a type of generative model that can generate realistic synthetic images. The code is implemented in Python and is available as a Colab notebook.