This Repo contains tools that allow us to import, clean, manipulate, and visualize data —Includes Python libraries, like pandas, NumPy, Matplotlib, and many more to work with real-world datasets to learn the statistical and machine learning techniques.

Easily assemble and serverlessly consume modular AI pipelines through secure Python APIs.

Predicting Hepatocellular Carcinoma through Supervised Machine Learning

ml-commons provides a set of common machine learning algorithms, e.g. k-means, or linear regression, to help developers build ML related features within OpenSearch.

This code loads network data, preprocesses it, reduces dimensions with an autoencoder, and trains multiple classifiers (KNN, RF, LR, SVM) for anomaly detection.

Web app for used car price predictions

A random forest

This repository serves as a culmination of my studies, dedicated to housing meticulously crafted implementations of various models, including Decision Trees (DTs), Logistic Regression (Lgr), and more, meticulously developed from scratch.

Contains a few projects I completed while taking the Intro to AI course

Big Data Project - CMP2024 - Computer Engineering - Cairo University

The python scripts analyzes a cereal dataset and extracts meaningful results from it.

A simple AI in pure c++ to play the snake game

Using linear regression machine learning model to predict home prices.

This is the repository of my study in Machine Learning Zoomcamp from DataTalksClub.

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Explore the Kaggle Codes Repository for concise and powerful code snippets covering the essentials of data science and machine learning. From Kaggle competitions to real-world projects, discover insights into exploratory data analysis, machine learning models, feature engineering, and data science mathematics.

This repo is accompanying the publication: “Computational fingerprinting workflow for environmental source tracking of microplastic based on representative additives”

With task 4 added, the Flyzy project is now ad a decision has been made on which model is best to use.

This is a collection of all the machine learning techniques required in any machine learning project. It contains detailed descriptions, videos, book recommendations, and additional material to properly grasp all the concepts.