A Complete Learning Roadmap for Mastering AI and Machine Learning from Scratch

This open-source learning roadmap was published by omar-20067 on GitHub (original link: https://github.com/omar-20067/Roadmap-AI-and-ML-from-scratch, release date: 2026-05-28). It aims to help beginners and advanced learners build a solid AI/ML knowledge system. It integrates high-quality free resources, solves common confusions faced by learners when dealing with massive resources, and provides a systematic learning path.

The AI/ML field is developing rapidly, with new papers, frameworks, and tools emerging every day, which overwhelms beginners. Common confusions include: Where to start? How deep do I need to go in math basics? How to balance theory and practice? Which resources are high-quality? This roadmap was created exactly to address these pain points—it is not just a pile of links but a carefully curated learning path.

The roadmap follows the principle of "step-by-step, practice-driven" and is divided into 5 stages:

1. Math and programming basics: Linear algebra, calculus, probability and statistics (the cornerstone of AI algorithms) + Python (the standard language for AI);
2. ML basic concepts: Supervised/unsupervised learning, model evaluation, overfitting/underfitting, etc., to cultivate algorithm intuition;
3. In-depth study of classic algorithms: Linear regression, decision trees, SVM, clustering, etc., to understand principles and applicable scenarios;
4. Introduction to deep learning: Core technologies such as neural networks, backpropagation, CNN, RNN;
5. Advanced topics and practice: NLP, computer vision, reinforcement learning, as well as engineering skills like model deployment and MLOps. Each stage marks prerequisite knowledge requirements to avoid entering advanced content with a weak foundation.

The roadmap strictly screens resources, marking difficulty levels, estimated learning duration, and prerequisites. All recommended resources are free, including: well-known university open courses (e.g., Stanford CS229, Andrew Ng's ML course), free electronic versions/lectures of classic textbooks, high-quality blogs and technical documents, practical projects, and code repositories. The "open-source first" concept lowers the learning threshold, allowing more people to access high-quality resources.

• Computer science background: Quickly browse math basics, focus on supplementing probability and statistics, and directly enter ML algorithm learning;
• Math/statistics background: Strengthen programming practice and consolidate theory through project-driven learning;
• Complete beginners: Follow the order strictly, and enter the next stage only after completing the exercises and projects of each stage.

As an open-source project, the roadmap welcomes community contributions. Learners can submit better resources, solutions to learning bottlenecks, etc., via Pull Request. The crowdsourcing model ensures that the roadmap keeps up with the pace of technological development.

In today's era of overflowing AI resources, a systematic roadmap is more valuable than scattered tutorials. This project not only provides a clear path but also establishes a "systematic learning" mindset. It is recommended to use it as a learning map, regularly review progress, and adjust the pace. Mastering AI is a marathon—continuous learning and practice are the keys.