AI and ML Roadmap

A step-by-step AI and ML roadmap with 7 role-based learning paths (AI Engineer, Data Scientist, FDE and more), from Python and math to LLMs and agents.

Each path lists its tracks in the order to take them, so every track builds on the one before. Tracks are made of interactive modules with live visualizations, quizzes, and runnable Python, and Pro learners who finish a path earn a verifiable certificate.

Choose your roadmap

• AI Engineer Roadmap

  The AI Engineer roadmap: deep learning, large language models, agents, and production deployment, sequenced so each track builds on the last.

  Tracks in order: Python for ML, AI Tools, Git & Version Control, Jupyter & Colab, SQL for Data, Foundations, Statistics, Probability Theory, Linear Algebra, Calculus, Optimization, Information Theory, ML Algorithms, Deep Learning, LLM, Software Engineering for AI, Agentic AI & Multi-Agent Systems, Inference Engineering, MLOps & Deployment, ML Pipeline, AI on Cloud, Interview Q&A, AI System Design.

• Forward Deployed Engineer Roadmap

  The Forward Deployed Engineer roadmap: embed with customers and ship production AI in their environment. Finish it on Pro for a verifiable certificate.

  Tracks in order: Forward Deployment Engineering, Python for ML, SQL for Data, AI Tools, Git & Version Control, Jupyter & Colab, LLM, Foundations, Linear Algebra, Statistics, ML Algorithms, Deep Learning, Software Engineering for AI, Agentic AI & Multi-Agent Systems, Inference Engineering, ML Pipeline, MLOps & Deployment, AI on Cloud, Interview Q&A, AI System Design.

• Software Engineer Roadmap

  The Software Engineer roadmap: add AI to the code you already write, weighted towards large language models, agents, and production engineering.

  Tracks in order: Python for ML, Jupyter & Colab, Git & Version Control, SQL for Data, Foundations, Statistics, Probability Theory, Linear Algebra, Calculus, Optimization, ML Algorithms, Deep Learning, LLM, Agentic AI & Multi-Agent Systems, Software Engineering for AI, Inference Engineering, MLOps & Deployment, ML Pipeline, AI Tools, AI on Cloud, Interview Q&A, AI System Design.

• Data Scientist Roadmap

  The Data Scientist roadmap: a full mathematical and statistical foundation, then machine learning end to end.

  Tracks in order: Foundations, Python for ML, SQL for Data, Git & Version Control, Jupyter & Colab, Probability Theory, Statistics, Linear Algebra, Calculus, Optimization, ML Algorithms, Deep Learning, Computer Vision, LLM, Agentic AI & Multi-Agent Systems, ML Pipeline, Information Theory, AI Tools, AI on Cloud, Interview Q&A, AI System Design.

• Data Engineer Roadmap

  The Data Engineer roadmap: SQL, modelling, orchestration and streaming, then the machine learning systems your data feeds.

  Tracks in order: Data Engineering, SQL for Data, Python for ML, Git & Version Control, Jupyter & Colab, Software Engineering for AI, Statistics, Foundations, MLOps & Deployment, AI on Cloud, ML Algorithms, LLM, Interview Q&A, Inference Engineering, AI System Design.

• Non-Tech / PM / Curious Roadmap

  The Non-Tech and PM roadmap: real AI judgement without deep coding, so you can spot opportunities, judge solutions, and use AI responsibly.

  Tracks in order: ML Algorithms, Deep Learning, LLM, Agentic AI & Multi-Agent Systems, AI on Cloud, AI for Product & Business, Interview Q&A, AI System Design.

• Student / Career Switcher Roadmap

  The Student and Career Switcher roadmap: a comprehensive foundation in mathematics, coding, machine learning, large language models, and agents.

  Tracks in order: Python for ML, Jupyter & Colab, Foundations, Statistics, ML Algorithms, ML Pipeline, SQL for Data, Git & Version Control, Linear Algebra, Probability Theory, Calculus, Optimization, Information Theory, Deep Learning, LLM, Agentic AI & Multi-Agent Systems, Software Engineering for AI, MLOps & Deployment, AI Tools, AI on Cloud, Interview Q&A, AI System Design.

Not sure where to start? Browse every track, then pick the path that matches the role you want.