General Assembly's 2015 Data Science course in Washington, DC

Machine Learning notebooks for refreshing concepts.

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.

an MLOps/LLMOps platform

AI-powered NBA game outcome predictor that uses advanced team stats and trend-based features to forecast winners and track model performance

CloudCV GSoC Ideas

UBC ARBERT and MARBERT Deep Bidirectional Transformers for Arabic

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

Measure and visualize machine learning model performance without the usual boilerplate.

Flexible tool for bias detection, visualization, and mitigation

A High-level Scorecard Modeling API | 评分卡建模尽在于此

An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP

This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price

An Interactive Approach to Understanding Deep Learning with Keras

Ollama Model Test - Figure out the best model for the task

A hands-on TensorFlow image recognition project teaching a computer to identify 10 everyday objects, originally for a linear algebra class, with tools to train a CNN, auto-tune settings, and test accuracy on random internet images.

Valor is a lightweight, numpy-based library designed for fast and seamless evaluation of machine learning models.

OpenLLM Monitor is a plug-and-play, real-time observability dashboard for monitoring and debugging LLM API calls across OpenAI, Ollama, OpenRouter, and more. Tracks tokens, latency, cost, retries, and lets you replay prompts — fully open-source and self-hostable.

PolyCouncil is an open-source multi-model deliberation engine for LM Studio. It runs multiple LLMs in parallel, gathers their answers, scores each response using a shared rubric, and produces a final, consensus-driven result. Designed for testing, comparing, and orchestrating local models with ease.