AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Open standard for machine learning interoperability

Parallel computing with task scheduling

Open Machine Learning Course

Fast and Accurate ML in 3 Lines of Code

A unified framework for machine learning with time series

Automated Machine Learning with scikit-learn

An open source python library for automated feature engineering

Flower: A Friendly Federated AI Framework

🍊 📊 💡 Orange: Interactive data analysis

Visual analysis and diagnostic tools to facilitate machine learning model selection.

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

Seamlessly integrate LLMs into scikit-learn.

Hummingbird compiles trained ML models into tensor computation for faster inference.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

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

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

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

High-Performance Symbolic Regression in Python and Julia