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PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Statistical and Algorithmic Investing Strategies for Everyone

JAX-based neural network library

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.

The machine learning toolkit for time series analysis in Python

a delightful machine learning tool that allows you to train, test, and use models without writing code

Visualize and compare datasets, target values and associations, with one line of code.

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

Shōgun

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

StellarGraph - Machine Learning on Graphs

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. https://deepnote.com/

Data manipulation and transformation for audio signal processing, powered by PyTorch

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Sequential model-based optimization with a `scipy.optimize` interface

POT : Python Optimal Transport

🧠 AI-powered enterprise search engine 🔎

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Learning to Rank in TensorFlow

Microsoft Distributed Machine Learning Toolkit

Control Any Computer Using LLMs.

Algorithms for explaining machine learning models

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.