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freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.
🎓 Path to a free self-taught education in Computer Science!
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
Learn how to develop, deploy and iterate on production-grade ML applications.
TranslatedGuidance for courses in Department of Computer Science and Technology, Tsinghua University
10 Weeks, 20 Lessons, Data Science for All!
Python training for business analysts and traders
TranslatedAI learning roadmap, compiling nearly 200 hands-on cases and projects, with free accompanying teaching materials, for beginners with zero background, focused on job-ready practice! Covers: Python, mathematics, machine learning, data analysis, deep learning, computer vision, natural language processing, PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv and other popular fields
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
TranslatedCrash Course Computer Science (5,090,000 views) (40 episodes in total, each 10 minutes) Translation completed in 2018. The comment section has a lot of praise
Open Machine Learning Course
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
TranslatedFree interactive 3D science lessons for high school · 铜匠实验室 (Tongjiang Lab, "Coppersmith Laboratory") · https://lab.tongjiang.app
Readings in applied data science
Don't memorize ML. Understand it from first principles including Inference, Cuda, Deep Learning, Data Science and Reinforcement Learning
Materials for GWU DNSC 6279 and DNSC 6290.
Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA