AI Specialization Program
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Soft AI Academy
AI Specialization Program
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Technical Tracks
AI Engineering & Data
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Week 1
WEEK 1:
Foundations of AI Engineering
Theme: Thinking Like an AI Engineer.
This week builds mindset + core technical foundation.
Students move from:
AI user → Engineering thinker.
Week 1 6 Lessons Structure
1. What is AI Engineering?
Difference between AI user vs AI engineer
AI system architecture overview
Data → Model → Evaluation → Deployment pipeline
Real-world production thinking.
2. Math for Machine Learning (Practical Only)
Vectors & matrices (intuitive understanding)
Matrix multiplication in ML
3. Math for ML
Probability basics
Mean, variance, distributions
Debugging.
Why math matters in debugging models
(No heavy theory — applied understanding only.).
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4. Python for AI Systems
Python refresher
Functions & modular coding
Numpy basics
Writing clean ML-ready code.
5. Data Structures & Algorithms (AI-Relevant Only)
Lists, dictionaries, sets
Complexity basics (why efficiency matters)
Searching & sorting intuition
Working with large datasets mindset.
6. Git & GitHub Workflow
Why version control matters
Repositories
Commits & branches
Basic collaboration workflow
Structuring AI projects properly.
Week 1 Outcome
Students can:
✔ Think in system flow
✔ Write structured Python code
✔ Understand ML math intuition
✔ Use Git properly
✔ Structure an AI project
They are now engineering-aware.
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