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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.
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.
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 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 repository contains code for predicting house sales prices using machine learning models. It includes data preprocessing, model training, evaluation, and prediction on test data.
Successfully established a machine learning regression model which can estimate the gross Black Friday sales for a particular customer, based on a distinct set of related and meaningful features, to a fair level of accuracy.
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.
The enhancement of Intelligent Transport Systems (ITS) involves the precise prediction of bike-trip durations, incorporating a comprehensive consideration of Seoul's weather conditions.
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.