AI Engineering Roadmap
The guided path to becoming an AI engineer through 3 core disciplines.
Practical knowledge and skills you'll need to build real AI systems.
Mathematics
Build a strong mathematical foundation essential for understanding ML algorithms
Software Engineering
Develop programming skills to implement AI solutions efficiently
Machine Learning
Master ML theories, algorithms, and specialized AI applications
Mathematics
Mathematical foundations are the building blocks of AI. These courses cover linear algebra, calculus, probability, and statistics - all essential for understanding how modern ML algorithms work.
Why Mathematics Matters
From linear algebra that powers neural networks to probability theory behind predictions, mathematics forms the foundation of every AI algorithm.
Understanding these concepts is essential to move beyond simply using AI tools to creating and optimizing them.
Foundation for all ML algorithms
Essential for model understanding
Key to optimization techniques
Getting Started
Begin with these fundamental courses that build your mathematical foundation for machine learning.
Intermediate Skills
Deepen your mathematical understanding with these essential courses for advanced ML concepts.
Advanced Topics
Explore cutting-edge mathematical concepts that power the latest advancements in AI.
Ready to explore more?
This roadmap highlights the featured courses in each discipline. Browse the complete course to find the perfect starting point for your AI journey and discover specialized topics.
Frequently Asked Questions
Do I need to learn all three disciplines?
Yes, the essential courses from each area are necessary for a well-rounded AI engineer.
Mathematics provides the theoretical foundation, software engineering gives you the tools to implement solutions, and machine learning brings it all together with specialized algorithms and techniques.
What level of math do I need?
College-level math is a good starting point. Linear algebra, calculus, probability, and statistics form the core mathematical foundation for AI.
Don't worry if you're rusty, the foundational courses are designed to build these skills step by step, focusing specifically on the concepts most relevant to AI.
What order should I learn these disciplines?
You can approach the three disciplines in any order based on your background and interests. What's most important is following the skill progression within each area.
Start with foundational courses before moving to intermediate and advanced topics. Many students begin with programming basics, then add mathematical concepts as they explore machine learning.
Do I need to learn to code?
Yes, absolutely.
As Greg Brockman, co-founder of OpenAI, noted: "Great AI engineers are first and foremost great software engineers."
AI is essentially a specialized field of software engineering, not a separate discipline. Programming skills are non-negotiable for implementing algorithms, processing data, and deploying models in production environments.
What if a course turns out not to be useful for me?
Your learning path will be unique to your goals and background. Not every course will feel immediately relevant, but seemingly unrelated concepts often become valuable later in unexpected ways.
These "battle scars" from working through challenging material build a deeper intuition that pays dividends when tackling complex problems. Trust the process and focus on building a broad foundation.