SharifiZarchi/Introduction_to_Machine_Learning Alternatives

Choose for the classic Stanford Coursera ML assignments, though exercises are in MATLAB rather than Python.

Machine learning-Stanford University

Choose if you want a maintained MIT-licensed open ML course with Jupyter notebooks instead of a university-specific syllabus.

Open Machine Learning course

Choose if you specifically want deep-learning instruction with PyTorch and modern tooling rather than a broad ML theory course.

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

Choose if you want an ML engineering/MLOps curriculum with deployment projects rather than a theory-and-notebooks course.

Learn ML engineering for free in 4 months! Register here 👇🏼

Choose if your goal is LLM-specific material and Colab notebooks rather than foundational ML.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Choose if you want reinforcement-learning lecture notes and exercises instead of a general ML syllabus.

Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University

Choose if you need a beginner-paced, broader AI curriculum with Microsoft-maintained lessons and labs.

12 Weeks, 24 Lessons, AI for All!

Choose if you need Chinese-language ML theory proofs and case studies, but it is a book companion rather than a full course with exercises.

《机器学习理论导引》（宝箱书）的证明、案例、概念补充与参考文献讲解。

Choose if you want a Python ML course, but the notebooks have been unmaintained for over 18 months.

:speech_balloon: Machine Learning Course with Python:

Choose if you only need lecture-note summaries; it lacks exercises and has not been updated in over two years.

🎓 Sharing machine learning course / lecture notes.

Choose for a Chinese-language ML book/cheat-sheet collection, but it is over six years stale.

《机器学习宝典》包含：谷歌机器学习速成课程（招式）+机器学习术语表（口诀）+机器学习规则（心得）+机器学习中的常识性问题 （内功）。该资源适用于机器学习、深度学习研究人员和爱好者参考！

Choose for Stanford CS229 2018 notes and problem sets, but it is an archival snapshot with no recent fixes.

All notes and materials for the CS229: Machine Learning course by Stanford University

Choose only for historical TensorFlow 1.x deep-learning examples; it is archived and long outdated.

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

Choose if you prefer the Zero to Mastery ML/DS syllabus, but the materials are over 18 months without updates.

All course materials for the Zero to Mastery Machine Learning and Data Science course.

Choose if you need a course on deploying serverless ML prediction services, but it is unmaintained for over 18 months.

Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features