AI Engineer Roadmap 2025

From Mathematical Foundations to deploying Large Language Models (LLMs) and Generative AI.

1. The Fundamentals (Math & Python)

💡 Teacher's Tip: Start here. You cannot do AI without Linear Algebra (Matrices) and Probability. Python is the language of AI. Master NumPy and Pandas libraries.
📚 Free Resources: Khan Academy Linear Algebra Kaggle Python Course
PythonStatisticsLinear Algebra

2. Machine Learning Core

💡 Teacher's Tip: Learn "Classical" ML before Deep Learning. Understand how a machine "learns" using algorithms like Regression and Decision Trees using Scikit-Learn.
📚 Free Resources: Andrew Ng's ML Specialization (Free Audit) Scikit-Learn Docs
Scikit-LearnRegressionClassification

3. Deep Learning & Neural Networks

💡 Teacher's Tip: Now we simulate the brain. Learn PyTorch (preferred over TensorFlow). Understand CNNs for images and RNNs for text data.
📚 Free Resources: Fast.ai (Practical Deep Learning) PyTorch Official Tutorials
PyTorchNeural NetworksComputer Vision

4. Generative AI & LLMs

💡 Teacher's Tip: The modern era. Learn the "Transformer" architecture. Learn how to fine-tune models like LLaMA and GPT using HuggingFace.
📚 Free Resources: HuggingFace NLP Course Intro to LLMs (Andrej Karpathy)
TransformersHuggingFacePrompt Engineering

5. MLOps & Deployment

💡 Teacher's Tip: An AI model is useless in a notebook. Learn to deploy it as an API using FastAPI, Dockerize it, and run it on the cloud.
📚 Free Resources: Full Stack Deep Learning FastAPI Documentation
DockerFastAPIAWS SageMaker