Foundational AI/ML Concepts
Foundational AI/ML Concepts provides product managers with the essential knowledge to navigate the rapidly evolving landscape of artificial intelligence and machine learning. This program cuts through the hype, delivering clear, actionable insights into how these powerful technologies work and how they can be leveraged to build superior products.
What You Will Learn
In today’s competitive landscape, a fundamental understanding of Artificial Intelligence and Machine Learning is no longer optional for product leaders—it’s imperative. This course is meticulously designed to equip product managers with the conceptual framework needed to engage confidently with technical teams, make informed strategic decisions, and drive innovation.
You will explore the core distinctions between AI and ML, understand their practical applications in real-world products, and gain insight into the ethical considerations that shape their development. We move beyond buzzwords to establish a robust knowledge base, ensuring you can identify opportunities, mitigate risks, and communicate effectively about AI/ML initiatives.
This program focuses on practical relevance, ensuring that every concept is presented with its implications for product strategy and execution. By demystifying the underlying mechanics of machine learning models, you will develop the acumen to translate complex technical capabilities into tangible product value and user experiences.
Module 1: Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know
This module clarifies the core definitions of AI and ML, their historical context, and their strategic relevance for product innovation.
2 Lessons
Module 2: How Machine Learning Models Learn: A Peek Under the Hood
Dive into the fundamental principles of how machine learning models are trained, evaluated, and deployed, without requiring deep technical expertise.
5 Lessons
Curriculum
- 2 Sections
- 7 Lessons
- Lifetime
- CHAPTER 1: Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know2
- CHAPTER 2: How Machine Learning Models Learn: A Peek Under the Hood5
- 2.1The Learning Process: Training, Validation, and Testing10 mins
- 2.2How Models Learn: An Example with k-Nearest Neighbors (k-NN)10 mins
- 2.3Evaluating Model Performance10 mins
- 2.4Overfitting and Underfitting: Striking the Right Balance for Real-World Performance10 mins
- 2.5Human-in-the-Loop: Blending AI Power with Human Expertise10 mins
Instructor
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