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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.
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.
The enhancement of Intelligent Transport Systems (ITS) involves the precise prediction of bike-trip durations, incorporating a comprehensive consideration of Seoul's weather conditions.
The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.
Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.
Successfully developed a machine learning model which can accurately predict the strength of cement based on various features such as blast furnace slag, water, coarse aggregate, etc.
Successfully established a machine learning model to accurately predict the price of a flight in India based on several features such as duration, days left, arrival time, departure time and so on.
Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.
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.
University Admission Predictor is a sophisticated Flask-based web application designed to predict the likelihood of admission to graduate programs based on student profiles. It leverages a range of regression techniques to evaluate admission chances.This project showcases the practical application of machine learning in educational forecasting.
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.
Successfully established a machine learning model which can accurately predict the expected life duration of a human being based on several demographic features such as alcohol consumption per capita, average BMI of entire population, etc.
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.