M

ML

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

A conversational AI chatbot built using Next.js and Node.js with text + voice chat, GitLab CI/CD, SAST security, issues, labels, milestones, and merge request workflow.

🛒 AI chat & product/category summaries in Amazon shopping, powered by the latest LLMs

Fundamental theory and practice in Machine Learning (ML) and Data Science (DS). 🧮

A set of data science and machine learning projects exploring various datasets — a place to test ideas, models, and analytical approaches.

ML model training

A comprehensive Python toolkit for analyzing protein structures and small molecules using real datasets from RCSB PDB and FDA-approved drugs.

A libre smart powered comic book reader for Android.

❗Note: This is a mirror. Check GitHub repository.

A comprehensive machine learning pipeline for classifying astronomy images into 6 categories of celestial objects, featuring advanced data preprocessing, exploratory data analysis, and deep learning classification models.

https://huggingface.co/spaces/Saqib772/Astronomy_image_classfication

Kaggle Notebook: https://www.kaggle.com/code/saqibiqbal2/astronomy-image-classification

A fully documented, GPL-licensed Large Language Model, that respects your freedoms while teaching you how it works

A Computer Vision algorithm for Malaria parasite detection and classification in digital images of thick blood smears.

Here’s the source code for my exploratory data analysis and model training for a movie recommendation system. The main model deployment code is in this repository.

Deployment Repo: (https://gitlab.com/aydie/ml-model-netflix-recommendation-system)

Website: aydie.in Contact: business@aydie.in

House Prices Competition on Kaggle

The main project for OctoMY™ - Ready-to-run robot software

Een MLFlow wrapper die het makkelijk maakt om samen in dezelfde MLFlow omgeving te werken met bijvoorbeeld Teams. Maakt het daarnaast makkelijker om met standaard machine learning packages (nu alleen nog scikit-learn) modellen en scores te loggen.

A comprehensive exploration of various machine learning algorithms, including supervised, unsupervised, and reinforcement learning methods. This project will implement, analyze, and optimize algorithms like decision trees, random forests, SVMs, and neural networks, providing hands-on experience in selecting and applying them for different use cases.

Usefulls statistical fonctions and machine learning helpers found during my courses or lectures or exemples

Третья практическая работа по ТАД'у 🧑‍🎤

My personal webpage