Sharing this in case it helps other beginners map out the journey.
Good evening, Copilot says:
Here’s a beginner‑friendly roadmap for anyone who wants to grow into an AI Engineer. This isn’t tied to any specific platform — it’s simply a structured way to navigate commonly used learning paths. The value is in the sequence, not the provider.
This roadmap is written for the AI Engineer role — someone who builds and deploys practical AI systems end‑to‑end. It’s a technical path focused on Python, ML, deep learning, LLMs, RAG, evaluation, and deployment. Just to avoid confusion: this is different from labels like ‘AI Product Engineer’ or ‘Model Architect,’ which are used in some community tools as learning personas. The AI Engineer track here reflects the broader, industry‑aligned skill set that many learners aim for.
AI Engineer Roadmap (Beginner‑Friendly)
A clear, logical progression from foundations → ML → deep learning → production → LLM engineering
Full Roadmap Table
| Stage | Course / Track | Duration | Purpose / Focus Area (Expanded) |
|---|---|---|---|
| 1A | Mathematics for Machine Learning & Data Science (C) | ~3 months | ML‑ready math: vectors, matrices, derivatives, probability, statistics, optimization. Builds intuition for how models learn and why they behave the way they do. |
| 1B | Python for Everybody (C) | ~1 month | Python fundamentals: variables, loops, functions, scripting. Establishes the programming base for AI work. |
| 1C | Data Analysis with Python (IBM) | ~1 month | NumPy, Pandas, Matplotlib. Teaches data cleaning, manipulation, and visualization — essential before training ML models. |
| 2 | Machine Learning Specialization (C/DL) | ~2 months | Core ML concepts: regression, classification, regularization, bias/variance, ML pipelines. Forms the “grammar” of AI. |
| 3 | Deep Learning Specialization (C/DL) | ~3 months | Neural networks, CNNs, RNNs, sequence models, optimization. Foundation of modern AI and deep learning intuition. |
| 4 | Machine Learning Engineering for Production (MLOps) (C/DL) | ~2 months | Deployment, pipelines, monitoring, data drift, model versioning. Teaches real‑world ML engineering and production constraints. |
| 5A | Generative AI for Everyone (C/DL) | ~2 weeks | Gentle introduction to generative AI concepts, capabilities, risks, and practical use cases. |
| 5B | Generative AI with Large Language Models (C/DL) | ~1 month | LLM fundamentals, responsible use, and building simple LLM applications. |
| 6A | Functions, Tools & Agents with LLMs (DL) | ~2 weeks | Tool‑calling, APIs, workflows, LangChain basics. Teaches how to connect LLMs to real systems. |
| 6B | AI Agents in LangGraph (DL) | ~2 weeks | Multi‑step agents, orchestration, stateful workflows. Covers advanced agent patterns used in production. |
| 7 | Fine‑Tuning Large Language Models (DL) | ~2 weeks | Adapting LLMs to tasks, datasets, and domains. Covers supervised fine‑tuning and parameter‑efficient methods. |
| 8 | Automated Testing for LLMOps (DL) | ~2 weeks | Testing AI behavior, detecting regressions, enforcing reliability and safety. |
| 9 | Generative AI for Software Development (DL) | ~2 weeks | AI‑assisted coding, debugging, documentation, and automation. Shows how AI integrates into real software workflows. |
Total Duration Summary (Category‑Aligned)
| Category | Included Stages | Approx Duration | What This Covers |
|---|---|---|---|
| Foundations | 1A, 1B, 1C | ~5 months | Math, Python, and data analysis — the essential base layer for all AI work. |
| Core ML + Deep Learning | 2, 3 | ~5 months | Classical ML + neural networks — the core AI engineering skillset. |
| Production + Generative AI | 4, 5A, 5B | ~3.5 months | MLOps, deployment, and foundational generative AI/LLM concepts. |
| LLM Engineering + Agents + Fine‑Tuning | 6A, 6B, 7, 8, 9 | ~2.5 months | Tool‑calling, agents, fine‑tuning, testing, and AI‑assisted development. |
 Total Estimated Duration |
— | ~16–17 months | Complete beginner → AI Engineer transformation. |
Footnote (Neutral & Forum‑Safe)
(C) indicates courses commonly found on Coursera. (DL) indicates courses created by DeepLearning.AI. Other providers (e.g., IBM) appear only when they fill a specific beginner‑friendly gap. Students are free to use equivalent courses from any provider — the roadmap focuses on the learning sequence, not the platform.