Two Paths to Build AI Skills — Choose the One That Fits Your Goals and Time.
👩💼 Track 1: Professional Roadmap (Light, Credibility-First)
Designed for busy professionals who can only spare 1–2 hours per week, but want real credibility fast.
Timeline: ~18 months (1–2 hrs/week)
Cost: ~$200 for first 10 months; $500–$800 total with cloud cert
Phases:
- Phase 1 (Months 1–4): Quick Wins
- Elements of AI (University of Helsinki, Free)
- IBM SkillsBuild badges (Free)
- Microsoft AI Fundamentals (AI-900 exam, $99)
- Google AI Essentials (Coursera, $49)
- Phase 2 (Months 5–10): Core Skills
- Python for Everybody (University of Michigan, ~$49–$98)
- Machine Learning Specialization (Stanford/DeepLearning.AI, ~$49–$98)
- Phase 3 (Months 11–18): Enterprise Readiness
- Deep Learning Specialization (optional, ~$98–$147)
- Enterprise cloud badge (Google Cloud ML Engineer, Azure AI Engineer, or AWS ML Specialty, ~$165–$300 exam)
✅ Outcome: Google, Microsoft, IBM, Helsinki, Michigan, and Stanford on your profile within 12 months, plus optional enterprise-level badge by 18 months.
👨💻 Track 2: Technical Roadmap (Advanced, Certification-Heavy)
Built for professionals who want deeper mastery — programming, math, advanced AI, and structured enterprise certifications.
Timeline: ~18–24 months (4–6 hrs/week ideal, but doable at 1–2 hrs/week stretched longer)
Cost: ~$500–$800 total
Phases:
- Phase 1: Foundations
- Python for Everybody (Michigan, ~$49–$98)
- Mathematics for Machine Learning (Imperial College London, ~$49–$98)
- Phase 2: Core Machine Learning
- Machine Learning Specialization (Stanford/DeepLearning.AI, ~$49–$98)
- AWS ML Specialty (optional, $300 exam)
- Phase 3: Deep Learning & Advanced AI
- Deep Learning Specialization (DeepLearning.AI, ~$98–$147)
- GANs Specialization (DeepLearning.AI)
- NLP Specialization (DeepLearning.AI)
- Phase 4: Enterprise Cloud
- Google Cloud ML Engineer (~$245 + $125–200 exam)
- OR Microsoft Azure AI Engineer (~$165 exam)
- OR AWS ML Specialty (~$300 exam)
✅ Outcome: University and top academic logos (Michigan, Imperial, Stanford, DeepLearning.AI) + advanced AI certs (NLP, GANs) + enterprise-ready cloud certification.
📊 Quick Comparison Table
| Professional Roadmap | Technical Roadmap | |
|---|---|---|
| Time Commitment | 1–2 hrs/week | 4–6 hrs/week (1–2 hrs if stretched) |
| Focus | Credibility + applied literacy | Deep technical AI engineering |
| Cost (Approx.) | $200 (first 10 months) / $500–$800 total | $500–$800 total |
| Early Wins | Google, Microsoft, IBM, Helsinki badges in 3–4 months | Python + Math foundation in 3–4 months |
| Final Outcome | Professional credibility + enterprise-ready badge | Advanced AI engineering skills + enterprise-ready badge |
🎯 How to Choose Your Track
- Choose the Professional Roadmap if you:
- Are short on time (1–2 hrs/week).
- Want credibility fast for LinkedIn, consulting, or leadership.
- Care more about using AI than building AI models from scratch.
- Choose the Technical Roadmap if you:
- Want to code, build, and deploy advanced AI systems.
- Are aiming for data science, ML engineering, or AI development roles.
- Don’t mind tackling math, deep learning, and structured exams.
👉 My recommendation: Many professionals start on the Professional Roadmap for quick wins, then graduate into the Technical Roadmap once they’ve built confidence and carved out more time.
