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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.
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 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.
Aditya Marketing is facing low response rates to their marketing campaigns. The objective of this project is to conduct thorough Exploratory Data Analysis, extracting insights through univariate and bivariate analysis. And Recommended strategic customer targeting tactics.
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
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.
Explore advanced neural networks for crafting captivating headlines! Compare LSTM 🔄 and Transformer 🔀 models through interactive notebooks 📓 and easy-to-use wrapper classes 🛠️. Ideal for content creators and data enthusiasts aiming to automate and enhance headline generation ✨.
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.
This repository contains code for predicting house sales prices using machine learning models. It includes data preprocessing, model training, evaluation, and prediction on test data.