Fundamentals of Applied Artificial Intellgience #254003

Upon completion of this course, participants will be able to:

1. Define core AI concepts and distinguish between AI, machine learning, and deep learning.

2. Identify real-world AI applications across industries such as healthcare, finance, and retail.

3. Use no-code/low-code tools to build and deploy simple AI models.

4. Evaluate AI solutions for feasibility, scalability, and ethical implications.

5. Collaborate with technical teams to design AI-driven projects.

6. Develop a strategy for integrating AI into business processes.

This course is ideal for:

• Business Professionals and Managers seeking to leverage AI.

• Project Managers and Product Owners involved in AI initiatives.

• Marketing, Sales, and Operations Teams exploring AI applications.

• Entrepreneurs and Innovators looking to integrate AI into their ventures.

• Non-Technical Professionals curious about AI’s practical uses.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Introduction to AI and Its Landscape

• Module 1: What is AI? History, Types, and Modern Applications.

• Module 2: AI vs. Machine Learning vs. Deep Learning: Key Differences.

• Module 3: The AI Ecosystem: Tools, Platforms, and Key Players.

• Hands-On Activity: Exploring AI in Everyday Life (e.g., Virtual Assistants, Recommendations).

• Case Study: How Amazon Uses AI for Personalization.

Day 2: How AI Works – Simplified

• Module 4: Data: The Fuel of AI – Collection, Labeling, and Preprocessing.

• Module 5: Machine Learning Basics: Supervised, Unsupervised, and Reinforcement Learning.

• Module 6: Introduction to Neural Networks and Deep Learning.

• Hands-On Lab: Building a Simple Model with a No-Code Tool (e.g., Teachable Machine).

• Workshop: Identifying Data Requirements for an AI Project.

Day 3: AI in Action – Industry Applications

• Module 7: AI in Healthcare: Diagnostics, Drug Discovery, and Patient Care.

• Module 8: AI in Finance: Fraud Detection, Algorithmic Trading, and Customer Service.

• Module 9: AI in Retail: Inventory Management, Customer Insights, and chatbots.

• Group Discussion: Brainstorming AI Use Cases in Participants’ Industries.

• Case Study: How Netflix Uses AI for Content Recommendations.

Day 4: Implementing AI Solutions

• Module 10: Designing an AI Project: From Problem Definition to Deployment.

• Module 11: Evaluating AI Tools: Off-the-Shelf vs. Custom Solutions.

• Module 12: Ethics and Responsibility: Bias, Privacy, and AI Governance.

• Hands-On Lab: Designing a Chatbot for Customer Support.

• Workshop: Assessing the Ethical Implications of an AI Application.

Day 5: Strategy and Future of AI

• Module 13: Building an AI Strategy: Aligning AI with Business Goals.

• Module 14: The Future of AI: Trends, Challenges, and Opportunities.

• Module 15: Leading AI Projects: Collaboration and Communication Tips.

• Capstone Project: Developing an AI Proposal for a Real-World Problem.

• Course Wrap-Up: Q&A, Certificates, and Next Steps.