12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- ml
- data-science
- machine-learning
- machine-learning-algorithms
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Python Data Science Handbook: full text in Jupyter Notebooks
Learn how to develop, deploy and iterate on production-grade ML applications.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
10 Weeks, 20 Lessons, Data Science for All!
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
This repository is maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org
Course Files for Complete Python 3 Bootcamp Course on Udemy
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 ;)
A game theoretic approach to explain the output of any machine learning model.
The fastai book, published as Jupyter Notebooks
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Translated[🔥updating ...] AI automated quantitative trading robot (fully local deployment) AI-powered Quantitative Investment Research Platform. 📃 online docs: https://ufund-me.github.io/Qbot ✨ :news: qbot-mini: https://github.com/Charmve/iQuant
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
💿 Free software that works great, and also happens to be open-source Python.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
Your new Mentor for Data Science E-Learning.
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑🔬
TranslatedChinese tutorial on reinforcement learning (Mushroom Book 🍄), read online at: https://datawhalechina.github.io/easy-rl/
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Python training for business analysts and traders