A hands-on approach to learning machine learning, with practical examples to grasp essential concepts.

This project uses machine learning models like Logistic Regression, Random Forest, and XGBoost to detect fraudulent credit card transactions. It handles class imbalance using SMOTE and visualizes key fraud patterns through an interactive Power BI dashboard.

A web-based application for Neural Style Transfer (NST), built with a unique tech stack that merges MERN with a Flask + Python + PyTorch backend for performing the actual style transfer using a pre-trained deep learning model.

A beginner-friendly introduction to data science and machine learning using Python with libraries like numpy, pandas, and sci-kit learn. The repository includes jupyter notebooks covering python basics, array operations, data visualization, preprocessing, classification, clustering, etc. It also contains implementation of a simple neural network.

This repository provides a conceptual foundation for understanding the basics of machine learning. It includes key concepts, algorithms, and examples to help beginners grasp the fundamentals of supervised, unsupervised learning, and more. Ideal for those starting their ML journey!

GML - Fast-Track to Machine Learning: A Curriculum Crafted for Newbies and Busy Bees

Basics y no tan basics de Machine Learning con Python