Hello Mentors and Community Members,
My name is Mahmood, and I am an AI Engineering student from Iraq. My primary goal is to acquire a deep technical understanding and master the skills required to become a professional AI/MLOps Engineer by 2026.
I have structured my learning path into the following stages, selecting these specific courses for their academic and technical depth.
The Roadmap (Focusing on Core Knowledge):
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Stage 1: CS Foundations: CS50’s Introduction to Computer Science (Harvard University).
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Stage 2: Python Mastery: Python for Everybody Specialization (University of Michigan).
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Stage 3: Data Handling: Introduction to Data Science in Python (University of Michigan).
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Stage 4: Math for AI: Mathematics for Machine Learning (Linear Algebra, Multivariate Calculus, PCA) (Imperial College London).
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Stage 5: Classical ML: Machine Learning CS229 (Stanford University).
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Stage 6: ML Engineering: Machine Learning Engineer Professional Certificate (Google).
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Stage 7: Deep Learning: Deep Learning Specialization (Neural Networks & CNNs) (DeepLearning.AI).
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Stage 8: Transformers & NLP: CS25: Transformers United V2 (Stanford University).
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Stage 9: Practical AI: Full Stack Deep Learning (FSDL).
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Stage 10: Cloud AI: Google Cloud Professional Machine Learning Engineer.
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Stage 11: Infrastructure: HashiCorp Certified: Terraform Associate.
**“Please feel free to suggest moving, removing, or adding any courses based on your industry experience to ensure this path is as efficient as possible for gaining real-world expertise.”**
My Questions for the Experts:
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As a student in Iraq, what specific projects should I focus on to be globally competitive?
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Is Terraform (Stage 11) still the industry standard for ML infrastructure in 2026, or should I look into Pulumi?
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Does this roadmap cover enough LLMOps and Vector Databases?
Thank you for your time and guidance!
Best regards,
Mahmood