Unlock the potential of finetuning Large Language Models (LLMs). Learn from industry expert, and discover when to apply finetuning, data preparation techniques, and how to effectively train and evaluate LLMs.

Successfully developed a machine learning model which can accurately predict whether a firm will become bankrupt or not, depending on various features such as net value growth rate, borrowing dependency, cash/total assets, etc.

This repository contains a project showcasing Federated Learning using the EMNIST dataset. Federated Learning is a privacy-preserving machine learning approach that allows a model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them.

An Image classifier model and builder for binary image classification.

The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

This is my public repository with mostly experimental code I write while exploring or creating various deep(or not so deep) neural networks.

Multi-class classification model to predict outcomes of cirrhosis patients using machine learning

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

This project, you will build a full AI pipeline for an image classification task using Convolutional Neural Networks (CNNs). The project will cover data ingestion, preprocessing, model training, deployment, and CI/CD integration using GitHub Actions, Docker, and AWS.

🏡 Empower property market decisions with a machine learning model predicting house prices using the Boston Housing dataset. 💸🏠💹

I deployed this bi-disease prediction model in python using Machine Laerning. Deployed this ML model as a web application on cloud streamlit. To see the model please visit

This project implements a movie recommendation service with Apache Spark using collaborative filtering.

Successfully established a supervised machine learning model which can accurately forecast the total weekly sales amount obtained at Walmart stores, based on a certain set of features provided as input.

Fake News Prediction Model

This project aims to train a predictive model to diagnose diabetes on women patients.

A Python implementation of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state they operate in.

NU Bootcamp Module 21

This project forecasts the total wind and solar electricity production using Long Short-Term Memory (LSTM) neural networks implemented in PyTorch. The model leverages time-series data to predict future renewable energy generation, helping to optimize energy management and grid stability.

In this project, I've created an end-to-end ETL pipeline and subsequently developed a machine learning model to predict the price of Amazon products based on several product-related features.

A real-time, end-to-end machine learning application built with Flask and integrated with MLflow for tracking and model management. The application predicts house prices based on user input, leveraging trained regression models and providing a web interface for seamless interaction.