A carefully curated collection of machine learning notes, resources, projects, and datasets designed to guide you through the ML landscape effectively.
Materials for the the Analyzing Time Series at Scale with Cluster Analysis in R Workshop
About Perplexify is an AI-powered search engine. It is an Open source alternative to Perplexity AI
Neural Dimensionality Tracker (NDT) is a production-ready Python library for high-frequency monitoring of neural network representational dimensionality during Neural Networks training
Car Detector using SVM and classical image processing techniques
Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers.
The first stage of face recognition models, this model was done only with the knn algorithm on the Olivetti faces dataset, which has reached a suitable accuracy.
Binary VPN Traffic Detection Using Wavelet Features and Machine Learning
🎨 ArtGenre-Classifier: Machine learning library developed from scratch in C with Python interoperability (ctypes). Complete application for classifying artistic movements (Cubism, Renaissance, Surrealism) using Linear, PMC, and RBF models.
An end-to-end serverless pipeline for Intelligent Document Processing (IDP) using Amazon Bedrock and Anthropic Claude 3 Sonnet. This project extracts structured data from scanned documents (e.g., PDFs, forms, invoices) using GenAI models, and stores results in a scalable cloud-native architecture.
End-to-End Customer Churn Prediction System | XGBoost + SHAP | FastAPI Backend | Streamlit UI | Power BI Analytics | Production-Ready ML Pipeline
Replate: The Recipe Waste Solution - Transforming Leftovers into Culinary Delights, Reducing Food Waste for a Sustainable Future!
End-to-end customer churn prediction with XGBoost, SHAP, Power BI & Flask API — deployed on Railway
An interactive, browser-based platform for no-code time-series analysis and forecasting. Upload data, run statistical tests, and visualize predictions from models like ARIMA, powered by a high-performance WebAssembly (WASM) engine.
This project uses Logistic Regression on the Wisconsin Breast Cancer Dataset (30 features, 569 observations) to predict tumor malignancy, integrated into a Streamlit web app for real-time diagnosis.
Bike rentals predictions by using live data from Api's with lightgbm regressor.
An AI-powered tool that analyzes workflows, identifies inefficiencies, and suggests automation opportunities using Large Language Models.
This project addresses the growing need for intelligent industrial maintenance systems. By applying machine learning techniques, we aim to detect anomalies in machine behavior, predict machine failures, estimate Remaining Useful Life (RUL), and schedule maintenance tasks based on priority—enhancing reliability and minimizing downtime.
My Machine learning and deep learning projects and templates
Domain adaptation of synthetic document images using neural networks
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