AI Fundamentals for Non-Data Scientists Course Syllabus

Overview: This course provides non-technical professionals with a strategic understanding of AI and machine learning fundamentals, focusing on real-world business applications. Over approximately 8 hours, learners will explore core concepts, evaluate models, apply no-code tools, and consider ethical implications—all without requiring coding experience. The course blends conceptual learning with hands-on exercises using accessible platforms, culminating in practical takeaways for leading AI initiatives in any industry.

Module 1: Big Data and AI Foundations

Estimated time: 2 hours

• Big Data concepts and their role in AI
• Data management tools and infrastructure basics
• Core AI and machine learning terminology for business leaders
• Case study analysis on data-driven decision making
• Developing a high-level data strategy

Module 2: Training & Evaluating ML Algorithms

Estimated time: 2 hours

• Overview of key ML algorithms: logistic regression, decision trees, neural networks
• Understanding loss functions and model training
• Evaluating models using precision, recall, and trade-offs
• Interpreting confusion matrices and ROC curves
• Running an AutoML experiment

Module 3: AI Applications & Emerging Methods

Estimated time: 1 hour

• Introduction to natural language processing (NLP)
• Basics of generative models: GANs and VAEs
• No-code machine learning with Teachable Machine
• Building and comparing two prototype models using sample datasets

Module 4: Industry Insights & Ethics

Estimated time: 1 hour

• Executive perspectives on AI deployment at scale
• Data privacy considerations in AI systems
• Identifying and mitigating bias in models
• Conducting an ethical AI health check in a simulated business scenario

Module 5: Generative AI Overview

Estimated time: 2 hours

• Understanding foundation models and their capabilities
• Fundamentals of prompt engineering
• Exploring creative and business use cases for generative AI
• Crafting and testing prompts for text generation
• Evaluating quality and relevance of AI-generated content

Module 6: Final Project

Estimated time: 1 hour

• Design a no-code AI solution for a real-world business problem
• Apply ethical review principles to the proposed solution
• Present a brief strategy memo outlining implementation and expected impact

Prerequisites

• Familiarity with basic business concepts
• No prior coding or data science experience required
• Access to a modern web browser for no-code tool use

What You'll Be Able to Do After

• Explain core AI and machine learning concepts in business terms
• Evaluate and compare machine learning models using performance metrics
• Build simple AI models using no-code and AutoML tools
• Apply ethical frameworks to AI deployment scenarios
• Lead AI strategy discussions and initiatives with technical and non-technical stakeholders