AI Foundations · 6 weeks
AI Foundations
Get fluent in what AI can and cannot do today. Read capability, data, model behavior, and risk signals before you commit budget, headcount, or a launch date.
Open with meVelorStrategy Academy · AI Learning
Welcome. Let's give you the working fluency of an AI practitioner and leader.
You don't need another newsletter. You need a tutor who can teach you a concept, walk you through a real decision, and help you produce work you can actually use on Monday morning. That's what I'm here for. Open any course, ask me anything, and I'll teach it the way a senior AI practitioner would explain it over coffee.
16Core AI courses you can take in any order
7Learning tracks across the AI stack
96Applied modules tied to real decisions
48Executive deliverables you'll produce
Curriculum at a glance
Sixteen courses, grouped the way you actually use them.
| Track | Courses you'll find here | Count |
|---|---|---|
| AI Foundations | AI Foundations · Applied Machine Learning | 2 |
| Generative AI and Agents | Generative AI and Large Language Models · Prompt Engineering · AI Agents and Automation | 3 |
| AI Engineering | Data Engineering for AI · MLOps and AI Platform · AI for Engineering Productivity | 3 |
| AI Strategy and Product | AI Product Management · AI Strategy and Transformation | 2 |
| Responsible AI | AI Ethics and Governance · AI Safety and Risk | 2 |
| AI in Function | AI in Marketing · AI in Operations · AI in Finance | 3 |
| Integration | Integrated AI Capstone | 1 |
Course library
Open any course — you can take them in any order.
AI Foundations · 7 weeks
Applied Machine Learning
Go from a business question to a working model — frame the problem, pick a family, evaluate without flattering yourself, and decide whether the model is good enough to ship.
Open with meGenerative AI and Agents · 7 weeks
Generative AI and Large Language Models
Get past the demos. Design a generative AI feature that is grounded in your data, evaluated honestly, and safe enough to put in front of customers.
Open with meAI Strategy and Product · 6 weeks
AI Product Management
Identify which AI bets are worth funding, design features that survive contact with users, and ship with the metrics, guardrails, and feedback loops a serious product needs.
Open with meAI Strategy and Product · 6 weeks
AI Strategy and Transformation
Build an AI strategy that holds up: a portfolio of bets, a theory of value, a transformation plan, and a governance posture that survives the board, the regulator, and your CFO.
Open with meAI Engineering · 6 weeks
Data Engineering for AI
Build the data foundation AI actually needs: pipelines that don't break, contracts that survive change, retrieval indexes that find the right thing, and quality controls that fire before the model does.
Open with meAI Engineering · 6 weeks
MLOps and AI Platform
Run AI in production the way you run anything else that matters: versioned, monitored, rollback-ready, and on-call. Build the platform and the playbooks.
Open with meGenerative AI and Agents · 5 weeks
Prompt Engineering
Write prompts that produce reliable, structured, grounded output — and treat them as the product surface they are, with evaluation sets and a change process.
Open with meGenerative AI and Agents · 6 weeks
AI Agents and Automation
Design agents that actually finish the task — with the right tools, the right controls, evaluation that pressure-tests reality, and oversight matched to the risk of what the agent can do.
Open with meResponsible AI · 5 weeks
AI Ethics and Governance
Make AI decisions that are fair, accountable, transparent, and lawful — with controls and reviews proportional to risk, and the documentation to defend them.
Open with meResponsible AI · 5 weeks
AI Safety and Risk
Find, measure, and mitigate AI failure modes before they ship — bias, robustness, security, misuse, emergent behavior — with the threat models, evaluations, and incident playbooks to back it up.
Open with meAI in Function · 5 weeks
AI in Marketing
Put AI to work across segmentation, content, channel orchestration, measurement, and experimentation — with the brand and risk controls a serious marketer needs.
Open with meAI in Function · 5 weeks
AI in Operations
Use AI to lift forecasting, scheduling, quality, anomaly detection, and automation — with the controls a real operating environment requires for service and safety.
Open with meAI in Function · 5 weeks
AI in Finance
Bring AI into forecasting, close, FP&A, audit, and risk — with the explainability, controls, and documentation a finance function and its auditors will actually accept.
Open with meAI Engineering · 5 weeks
AI for Engineering Productivity
Lift developer productivity with AI copilots, review, tests, and docs — without trading away code quality, security, or the team's craft.
Open with meIntegration · 8 weeks
Integrated AI Capstone
Bring it all together: ambition, portfolio, capability and data plan, organization and operating model, governance, and a value case — one integrated recommendation a board would actually act on.
Open with meReferences & further study
References & Additional Resources
The foundational books, articles, and papers behind the AI curriculum — spanning AI foundations, machine learning, deep learning, large language models, MLOps, and AI governance. Primary sources are favored throughout; use them to go beyond the lessons.
Foundational books
- Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Boston: Pearson. aima.cs.berkeley.edu
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press. deeplearningbook.org
- Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). Sebastopol, CA: O’Reilly Media.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.
- Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed. draft). web.stanford.edu/~jurafsky/slp3
- Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. New York: Viking.
- Huyen, C. (2022). Designing Machine Learning Systems. Sebastopol, CA: O’Reilly Media.
Articles & papers
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). “Deep Learning.” Nature, 521, 436–444.
- Vaswani, A., et al. (2017). “Attention Is All You Need.” Advances in Neural Information Processing Systems (NeurIPS) 30. arXiv:1706.03762
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” NAACL-HLT 2019. arXiv:1810.04805
- Brown, T., et al. (2020). “Language Models are Few-Shot Learners.” Advances in Neural Information Processing Systems (NeurIPS) 33. arXiv:2005.14165
- Mitchell, M., et al. (2019). “Model Cards for Model Reporting.” Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*). arXiv:1810.03993
- Sculley, D., et al. (2015). “Hidden Technical Debt in Machine Learning Systems.” Advances in Neural Information Processing Systems (NeurIPS) 28.
- Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). “Concrete Problems in AI Safety.” arXiv:1606.06565
Citations follow a consistent author–date house style. Books are listed with publisher; articles and papers cite journal/venue, with stable links (publisher, Stanford, or arXiv) where an authoritative open copy exists.