Roadmap to Mastering AI Engineering
From foundational concepts to advanced systems, this is your step-by-step guide to gaining the practical skills needed to build cutting-edge AI.
Stage 1: Foundational AI
Build core knowledge in mathematics, programming, data science, and the basics of AI and LLMs.
Mathematics & CS Foundations
Core
Python Programming Fundamentals
Linear Algebra Fundamentals for Machine Learning
Calculus Fundamentals for Machine Learning
Probability & Statistics Fundamentals for Machine Learning
Introduction to Databases
SQL for Data Science Fundamentals
Getting Started with Git
Core ML & Data Science
Core
Introduction to Data Science
Introduction to Machine Learning
Fundamentals of Model Evaluation and Metrics
Introduction to Data Cleaning and Preprocessing
Essential Numpy and Pandas
Data Visualization with Matplotlib and Seaborn
Electives
Introduction to Exploratory Data Analysis
Getting Started with Scikit-Learn
Introduction to Multimodal AI
Introduction to Autoencoders and Feature Learning
Specialization
Applied Autoencoders for Feature Extraction
Introduction to AI & LLMs
Stage 2: Applied AI
Get into deep learning, develop LLM applications, and learn the basics of ML engineering.
LLM & Generative AI Development
Core
Prompt Engineering and LLM Application Development
Getting Started with Retrieve-Augmented Generation (RAG)
Python for LLM Workflows: Tooling and Best Practices
ML Engineering & MLOps Basics
Core
Introduction to Data Engineering
Introduction to ETL Pipelines
Introduction to MLOps
Docker and Containerization for ML Projects
FastAPI for ML Model Deployment
Introduction to Machine Learning Deployment
Electives
Data Structures and Algorithms for Machine Learning
Planning and Optimizing AI Infrastructure
Data Versioning and Experiment Tracking for Machine Learning
Model Interpretability with SHAP and LIME
Specialization
Time Series Analysis and Forecasting
Stage 3: Advanced AI Systems
Master advanced LLM techniques, explore specialized AI applications, and scale systems with production MLOps.
Advanced LLM Techniques
Core
Fine-tuning and Adapting Large Language Models
Introduction to LLM Fine-Tuning
Specialized AI Applications
Core
Introduction to Computer Vision
Natural Language Processing Fundamentals
Introduction to Speech Recognition
Introduction to Reinforcement Learning
Electives
Applied Speech Recognition
Intermediate Reinforcement Learning Techniques
Specialization
Advanced CNNs for Computer Vision Applications
Advanced Reinforcement Learning Techniques
Advanced Transformer Architecture
Autoencoders and Representation Learning
Variational Autoencoders: Advanced Techniques and Representation Learning
Advanced Generative Adversarial Networks
Advanced Graph Neural Networks: Architectures and Implementation
Advanced Speech Recognition and Synthesis
Advanced Diffusion Model Architectures and Training
Advanced Synthetic Data Generation: GANs and Diffusion Models
Evaluating Synthetic Data Quality: Advanced Techniques
Production MLOps & Scalability
Electives
Advanced AI Infrastructure Design and Optimization
Advanced Feature Store Implementation for ML Systems
Advanced Python Programming for Machine Learning
Specialization
Deploying Quantized LLMs for Efficient Inference
Deploying Diffusion Models at Scale
Advanced TensorFlow Techniques
Advanced PyTorch
Advanced JAX: Performance, Optimization, and Scale
Advanced Adversarial Machine Learning
Advanced Bayesian Machine Learning
Advanced Federated Learning Techniques
Stage 4: Specialized AI Frontiers
Explore cutting-edge research in LLMs, advanced AI systems, and theoretical concepts.
Cutting-Edge LLM Research
Specialization
LLM Compression and Acceleration Techniques
Agentic LLM Systems and Memory-Augmented Architectures
Advanced LLM Alignment: Constitutional AI and RLAIF
Mixture of Experts: Advanced Architecture, Training, and Scaling
Large Scale Distributed Retrieval-Augmented Generation
Advanced AI Systems & Theory
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 complete all tracks in all stages?
While the foundational stages (especially Stage 1 and 2) provide a important foundation for any AI engineer, your path through advanced stages and specialized tracks can be tailored to your specific career goals and interests.
Focus on building a strong core, then explore areas that align with the type of AI work you want to do.
What level of math do I need?
A solid understanding of college-level mathematics (linear algebra, calculus, probability, and statistics) is highly beneficial.
Our foundational math courses are designed to build these skills, focusing on concepts most relevant to AI, even if you need a refresher.
What order should I follow the roadmap?
It's generally recommended to progress through the stages sequentially (Stage 1 to Stage 4), as later stages build upon concepts from earlier ones.
Within a stage, you can prioritize tracks based on your interests, but ensure you cover foundational concepts before tackling more advanced topics within that track or stage.
Do I need to learn to code?
Yes, absolutely. Programming skills are non-negotiable for implementing algorithms, processing data, and deploying models in production environments.
AI is essentially a specialized field of software engineering, not a separate discipline. Python programming and data manipulation libraries are essential tools every AI practitioner needs to master.
What if a course turns out not to be useful for me?
Learning is a journey of discovery. Not every single concept in every course will seem immediately applicable to your specific goals at that moment.
However, building a broad and diverse knowledge base in AI is invaluable. Seemingly unrelated topics can connect in surprising ways later on, and working through challenging material builds critical problem-solving skills and intuition. Trust the process and focus on continuous learning.