An Open Source Machine Learning Framework for Everyone
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
Open standard for machine learning interoperability
A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.
Low-code framework for building custom LLMs, neural networks, and other AI models
Build, Manage and Deploy AI/ML Systems
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
The Open Source Feature Store for AI/ML
Largest list of models for Core ML (for iOS 11+)
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
Superduper: End-to-end framework for building custom AI applications and agents.
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
modular quant framework.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Hopsworks - Data-Intensive AI platform with a Feature Store
Train and Deploy an ML REST API to predict crypto prices, in 10 steps
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Sharing Updatable Models (SUM) on Blockchain
CellViT: Vision Transformers for Precise Cell Segmentation and Classification