12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Apache Superset is a Data Visualization and Data Exploration Platform

scikit-learn: machine learning in Python

Deep Learning for humans

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

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Learn how to develop, deploy and iterate on production-grade ML applications.

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Streamlit — A faster way to build and share data apps.

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

10 Weeks, 20 Lessons, Data Science for All!

💫 Industrial-strength Natural Language Processing (NLP) in Python

500 AI Machine learning Deep learning Computer vision NLP Projects with code

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Roadmap to becoming an Artificial Intelligence Expert in 2022

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.

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

:memo: An awesome Data Science repository to learn and apply for real world problems.

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

The fastai book, published as Jupyter Notebooks

Data Apps & Dashboards for Python. No JavaScript Required.

Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

matplotlib: plotting with Python

Prefect is a workflow orchestration framework for building resilient data pipelines in Python.

Best Practices on Recommendation Systems

A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.