Become an AI Engineer from Scratch: A Complete Modern AI Learning Roadmap

This open-source learning roadmap was created by GitHub user uixPhuke (Rohon Borah) to help developers master modern AI technologies step by step. It covers a complete skill set including Python fundamentals, data analysis, machine learning, deep learning, Transformer architecture, generative AI, large language models, RAG systems, and AI engineering deployment. It emphasizes the systematicity and practicality of knowledge, with clear goals and recommended projects for each stage.

In today's era of rapid AI technology development, many developers want to transition to the AI field but feel overwhelmed by scattered resources. This roadmap was compiled by full-stack developer Rohon Borah based on his own experience of switching from the MERN stack to AI. It addresses the problem of unclear learning paths and provides a complete route from Python fundamentals to production-level AI system deployment, focusing on the systematicity and practicality of knowledge.

Python Fundamentals: Master data types and control structures, data structures (lists, tuples, dictionaries, etc.), object-oriented programming (classes and objects, inheritance, etc.), file and exception handling (file reading/writing, JSON processing).

Data Analysis: Core tools include NumPy (multi-dimensional array operations), Pandas (data cleaning and transformation), Matplotlib/Seaborn (visualization). Practice exploratory data analysis (EDA) to understand dataset characteristics.

Machine Learning: Covers supervised learning algorithms such as linear/logistic regression, decision trees, random forests, KNN, and SVM, using the Scikit-learn library. Emphasizes the importance of Kaggle practice.

Deep Learning: Starts with artificial neural networks (perceptrons, backpropagation), learns CNN (computer vision), RNN/LSTM (sequence data), and masters TensorFlow/Keras or PyTorch frameworks.

Transformer Architecture: The core is the self-attention mechanism, which solves the bottleneck of RNN sequential processing. Need to understand multi-head attention, positional encoding, etc.

Generative AI and LLMs: Includes large language models such as GPT and BERT. Master the pre-training-fine-tuning paradigm, prompt engineering, and embedding techniques.

RAG and Agents: RAG systems combine external knowledge bases to solve model hallucinations. AI agents involve ReAct patterns and tool calling; recommended tools include OpenAI API, Hugging Face, and LangChain.

API Development: Use Flask/FastAPI to encapsulate model services, handle request validation and performance monitoring.

Containerization: Use Docker to package applications to ensure environment consistency; Kubernetes for container orchestration.

MLOps Practices: Covers model version management, experiment tracking, automated testing, and CI/CD pipelines (e.g., GitHub Actions).

Recommended projects include:

• House price prediction (regression problem, feature engineering and model tuning)
• Spam detection (introduction to text classification)
• AI chatbot (dialogue system design)
• PDF question-answering system (application of RAG technology)
• Image classification (CNN reinforcement)
• Recommendation system (collaborative filtering and deep learning methods) Integrate knowledge from all stages through projects.

Learning Advice: Beginners should learn in the order of stages and not skip the basics; practice with projects at each stage; experienced developers can dive deep into specific areas as needed.

Summary: The roadmap provides a systematic learning framework. Basic principles are stable, so learners can expand their knowledge system based on this and maintain competitiveness in the AI era.