Fundamentals of Modern AI (TECH7000)

Description:

This comprehensive AI training program aims to introduce entry-level individuals to the fundamentals of Artificial Intelligence (AI) and its various applications. Students will explore key AI concepts, tools, and ethical considerations. The course includes lectures, demonstrations, and hands-on labs, concluding with a final exam to assess understanding and readiness to apply AI knowledge in their careers.

Microsoft Office is required software while taking this course.

Duration:

3 days

Level:

Foundational

Who Should Attend?

Entry-level individuals with basic internet and Microsoft Office/Google Workspace skills who are looking to transition into AI-related roles or enhance their career prospects with AI knowledge.

Credits:

1.8 CEUs

Learning Outcomes:

• Describe the historical evolution of AI, from early rule-based systems to modern machine learning and generative models.
• Explain core AI concepts, including machine learning, deep learning, and neural networks, and how they power today’s intelligent systems.
• Differentiate between traditional machine learning models and foundational models, including large language models (LLMs) and diffusion models used in generative AI.
• Explain how chatbots work and apply prompt engineering techniques -- including chain-of-thought prompting and retrieval-augmented generation (RAG) -- to improve interaction quality.
• Evaluate the role of AI assistants and copilots (e.g., Microsoft 365 Copilot, Adobe Acrobat Assistant) in enhancing digital workflows and user productivity.
• Demonstrate how embedded AI tools can automate or augment content creation, data processing, and communication.
• Identify popular languages, tools, and platforms used in AI development, such as Python, TensorFlow, AWS, etc.
• Describe how cloud computing supports scalable AI applications, and understand the role of APIs and model hosting in deploying AI solutions.
• Outline the key steps for planning and building an AI-ready organization, including data readiness, talent development, and process integration.
• Identify the risks associated with AI adoption, including bias, hallucination, explainability challenges, and data privacy concerns.
• Apply principles of responsible and ethical AI use, including fairness, transparency, accountability, and regulatory alignment.
• Describe how AI and machine learning are applied in cybersecurity, including threat detection, anomaly detection, and behavior analysis.
• Compare cybersecurity platforms and services (e.g., AWS security tools, enterprise SOC tools) that integrate AI for proactive defense and automation.

No sessions scheduled

Other Recommended Courses:

• Crafting AI Prompts: Prompt Engineering I (TECH7020)