Learning Paths¶
Choose a path aligned with your goals. Each path defines a chapter sequence, time estimate, and target audience.
Path A: Complete AI Engineer¶
Build expertise across all domains.
| Attribute | Value |
|---|---|
| Chapters | 1 → 2 → 3 → 4 → 5 → 6 → 7 → 8 → 9 → 10 → 11 → 12 → 13 → 14 → 15 → 20 |
| Total Time | ~110 hours |
| Duration | 3–4 months at 8–10 hours/week |
| Best For | Career changers, aspiring ML engineers, generalists |
| Outcomes | Full-stack AI engineering, systems thinking, deployment expertise |
Path B: Machine Learning Specialist¶
Deep expertise in ML theory and practice.
| Attribute | Value |
|---|---|
| Chapters | 1 → 2 → 3 → 4 → 6 → 7 → 8 → 9 → 15 → 19 → 20 |
| Total Time | ~100 hours |
| Duration | 3–4 months at 8–10 hours/week |
| Best For | Data scientists, ML engineers, researchers |
| Outcomes | Advanced ML techniques, optimization, production systems |
Path C: LLM & NLP Expert¶
Specialize in language and foundation models.
| Attribute | Value |
|---|---|
| Chapters | 1 → 5 → 10 → 11 → 12 → 13 → 14 → 17 → 20 → 23 |
| Total Time | ~90 hours |
| Duration | 3 months at 8–10 hours/week |
| Best For | NLP engineers, LLM application builders, prompt engineers |
| Outcomes | LLM expertise, RAG systems, fine-tuning, production NLP |
Path D: AI for Finance¶
Finance-specific AI expertise.
| Attribute | Value |
|---|---|
| Chapters | 1 → 3 → 4 → 6 → 7 → 21 → 19 → 20 → 23 |
| Total Time | ~85 hours |
| Duration | 3 months at 8–10 hours/week |
| Best For | Finance professionals, quants, fintech engineers |
| Outcomes | Financial ML models, trading systems, risk analysis |
Path E: Quick Start — AI Fundamentals¶
Fast track to functional AI knowledge.
| Attribute | Value |
|---|---|
| Chapters | 1 → 5 → 6 → 9 → 11 → 23 |
| Total Time | ~48 hours |
| Duration | 6 weeks at 8–10 hours/week |
| Best For | Quick learners, career explorers, busy professionals |
| Outcomes | Core AI concepts, ability to build simple AI applications |
In a Hurry?
Path E is the shortest route to practical AI skills while staying hands-on.
Path F: Executive / Manager¶
Understand AI at a high level for decision-making.
| Attribute | Value |
|---|---|
| Chapters | 5 → 6 → 20 → 22 → 23 → 25 |
| Total Time | ~38 hours |
| Duration | 1–2 months at 4–5 hours/week |
| Best For | Executives, managers, founders |
| Outcomes | Understanding of AI capabilities, limitations, strategic thinking |
Minimal Technical Depth
Path F focuses on concepts and strategy rather than hands-on implementation.
Path Comparison¶
| Path | Hours | Chapters | Focus |
|---|---|---|---|
| A | 110 | 16 | Full AI engineering |
| B | 100 | 11 | ML specialization |
| C | 90 | 10 | LLM & NLP |
| D | 85 | 9 | Finance |
| E | 48 | 6 | Quick start |
| F | 38 | 6 | Executive |
How to Use Learning Paths¶
- Choose your path based on your goal and starting point
- Run the interactive hub for a recommendation:
python interactive/berta.py paths - Start with the first chapter in your path
- Complete each chapter before moving on (or skip if you have equivalent experience)
- Adapt — paths are guidelines; mix and match as needed
Created by Luigi Pascal Rondanini | Generated by Berta AI