People ask for my certification roadmap.

Here's what I'd do if I were starting fresh in 2026, based on 240+ certifications and the perspective of knowing which ones actually mattered.

Months 1-2 (Foundation): Andrew Ng's ML Specialization and Google's ML Crash Course (both free). Build two small projects.

Months 3-4 (Cloud): Pick ONE — AWS ML Specialty OR GCP ML Engineer. Not both. Build a project on that cloud platform.

Months 5-6 (GenAI): NVIDIA Deep Learning courses and DeepLearning.AI's GenAI specialization. Build a RAG application.

Months 7-8 (MLOps): Databricks ML Professional. Learn Docker and basic Kubernetes. Deploy one of your previous projects properly.

Months 9-12 (Specialization): Go deep in a domain — healthcare, finance, computer vision, whatever excites you. Read and implement papers.

The critical rule: every certification should produce a project. If you can't build something with what you learned, the certification was wasted time.

Don't collect certificates. Accumulate capabilities. The certificate goes on LinkedIn. The project goes on GitHub. Guess which one gets you hired.