Grokking AI for Engineering & Product Managers Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
An essential guide for engineering and product leads—providing practical AI knowledge, real-world use cases, and ethical guidance to confidently drive AI-infused products and teams. This course is structured into four core modules totaling approximately 4.5 hours, with a final project to apply learning. Each module combines concise technical overviews with interactive quizzes and real-world context, designed for busy professionals.
Module 1: The Fundamentals
Estimated time: 2 hours
- AI, ML, and DL architectures overview
- Supervised, unsupervised, and reinforcement learning
- Introduction to deep learning: CNNs and RNNs
- NLP and transfer learning concepts
Module 2: AI in Practice
Estimated time: 1 hour
- Building trustworthy AI systems
- ML infrastructure and cloud platforms
- Overview of AI frameworks and tools
- Best practices for reliable AI deployment
Module 3: Real Case Studies
Estimated time: 0.75 hours
- Starbucks: AI in personalization
- Netflix: Recommendation engine insights
- American Express: Fraud detection systems
- Wildlife conservation: AI for social impact
Module 4: Responsible AI
Estimated time: 0.75 hours
- Ethical frameworks for AI decision-making
- Bias detection and mitigation strategies
- Transparency and regulatory considerations
Module 5: Final Project
Estimated time: 1 hour
- Design an AI strategy for a real-world product scenario
- Apply ethical and technical principles from the course
- Submit for peer reflection and feedback
Prerequisites
- Familiarity with basic product or engineering leadership concepts
- No coding experience required
- Interest in AI-driven product innovation
What You'll Be Able to Do After
- Explain core AI and ML concepts to technical and non-technical stakeholders
- Evaluate AI use cases for business impact and feasibility
- Lead AI initiatives with awareness of infrastructure and ethical considerations
- Apply best practices for trustworthy, user-centric AI products
- Communicate effectively with data science and ML engineering teams