A Go wrapper for hnswlib 📦

Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai

This repository contains Machine Learning Algorithms such as KNN, SVM, Trees, etc.

It is a Repo that contain different type of Machine Learning Algorithm like Regression ,classification and clustering that will be added soon

Philippine Students' Employability KNN Classification and Prediction

Machine Learning Models trained on Scikit-learn datasets. This repository contains the code files and saved models trained on Toy datasets (Classification & Regression), and Real World dataset.

A simple digit recognizer written using Python.

TiMBL implements several memory-based learning algorithms.

Developed exercises and practical tasks to help students grasp key machine learning topics in a course hosted by Birkar Academy and ICDS.ai

Tokenize and convert sample text data into vectors using BERT. Load the vector representation of the text to OpenSearch and use kNN for semantic search

Postgres with GPUs for ML/AI apps.

Promotion-Based Sales Forecasting: Optimizing Demand Planning

Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.

PostgreSQL vector database extension for building AI applications

The Traffic Monitor application allows users to calculate the shortest path between two points on a map and predict traffic conditions along that path.

This project applies the K-Nearest Neighbors (KNN) algorithm to predict iPhone purchases based on customer data. Using features like age, salary, and previous purchase behavior, the KNN model classifies customers into buyers and non-buyers.

k-NN Search Go pkg

A machine learning project for classifying bearing faults using the CWRU dataset, with models built using Python and various ML techniques such as cross-validation, PCA, tSNE, SVM, XGBoost.

This project uses Explainable AI (XAI) to interpret machine learning models for diagnosing faults in industrial bearings. By applying SVM and kNN models and leveraging SHAP values, it enhances the transparency and reliability of machine learning in industrial condition monitoring.

Integration with (approximate) nearest neighbors libraries for scikit-learn + clustering based on with kNN-graphs.