The Complete AIML Roadmap

Artificial intelligence and machine learning skills are among the most in-demand on the job market, yet most beginners don't know where to start. This AIML roadmap gives you a clear, ordered path from fundamentals all the way to production-ready AI engineering — without paying for expensive bootcamps or fragmented YouTube playlists.

Why follow a structured roadmap?

The problem with learning AIML on your own is the firehose of content. Courses on Coursera, YouTube channels, research papers, GitHub repos — it's overwhelming. A structured roadmap solves this by giving you a sequence: what to learn first, what depends on what, and what you can skip for now. It turns a chaotic subject into a series of achievable milestones.

What the roadmap covers

• →Foundations: Python, linear algebra, statistics, and probability. These are the building blocks everything else sits on.
• →Classical ML: Regression, classification, clustering, tree-based methods, and model evaluation. The workhorses of data science.
• →Deep Learning: Neural networks, CNNs, RNNs, attention mechanisms, and transformers. The engine behind modern AI.
• →LLMs & Agents: Working with large language models, prompt engineering, RAG, and building AI agents that take actions.
• →Production AI: Deployment, monitoring, MLOps basics, and building systems that scale.

How to use this roadmap

Start at the AI Engineer Roadmap or the Machine Learning Roadmap depending on your goal. Each topic has curated free resources attached — no paid content required. As you work through them, mark topics complete to track your momentum. Star the ones you want to revisit.

From learning to landing a job

The roadmap doesn't stop at theory. Once you've built your skills, browse AI/ML job listings updated daily, study the interview question bank to prepare for technical rounds, and check out the resources library for deeper dives into any topic. Everything is free and in one place.