This repository, named "ml_practice," serves as a learning ground for various machine learning concepts and techniques. The repository contains a collection of Python scripts and Jupyter notebooks, each dedicated to exploring and implementing different machine learning algorithms, models, and methodologies.

Beginner's tutorial for working with the Google Earth Engine Python API in Jupyter Notebooks. The tutorial walks through a GEE workflow of measuring urban development surrounding Denver International Airport. Future work will involve development of a linear regression model to predict urban development surrounding DIA. This repository serves as a capstone project submission for Earth Data Analytics Professional Certificate Final Project.

Hello and welcome to my Data Science portfolio! This repository serves as a central hub showcasing a collection of my projects demonstrating skills and experience in data analysis, machine learning, and artificial intelligence. Each project folder contains Jupyter Notebooks with detailed code, explanations, and visualizations.

This repository comprises Jupyter Notebooks that serve as supplementary material to the journal article titled "Review of Multifidelity Models." The notebooks contain Python-based implementations that demonstrate toy problems in the multifidelity domain.

My_homebrewed_NOTEBOOKS is a GitHub repository that houses a collection of personal notebooks derived from various sources, including Kaggle and Jupyter Notebooks. This repository serves as a curated collection of notebooks created and customized by the repository owner, providing a valuable resource for learning and exploring different topics.

Welcome to the "LLM Notebooks" repository, dedicated to exploring and understanding the capabilities of Large Language Models (LLMs). This collection of Jupyter notebooks serves as a comprehensive resource for experimenting with state-of-the-art language models, including but not limited to OpenAI's GPT-3.5.