Fundamentals of Modern AI
Overview
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.
Duration
3-4 days
Who Should Take This Course
Audience
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.
Prerequisites
Basic proficiency in internet navigation and Microsoft Office/Google Workspace. No prior AI knowledge is required.
Why You Should Take This Course
In the duration of this course, students will:
- Career Transition: Ideal for those seeking to start or advance a career in AI.
- Practical Skills: Hands-on experience with AI tools and technologies that are highly demanded in various industries.
- Comprehensive Coverage: Learn a wide range of AI applications, from general AI concepts to specialized tools and ethical considerations.
- Credential: Completion of the course and passing the final exam will provide a Certificate of AI Fundamentals competency.
By the end of the following course modules, participants will be able to:
AI Foundations
- 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.
Modern AI Architectures & Techniques
- 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.
AI in Productivity Tools
- 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.
Tools, Platforms & Infrastructure
- 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.
AI Strategy, Risk & Ethics
- 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.
AI in Cybersecurity
- 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.
Course Outline
Fundamentals of Modern AI
-
- (Brief) History of AI
- Key AI Concepts
o Machine Learning
o Deep Learning (Neural Networks)
o Foundational Models & Generative AI
- Chatbots & Prompt Engineering
o Chain-of-Thought
o Retrieval-Augmented Generation (RAG)
- Copilots & Assistants
o Copilot for Microsoft 365
o Adobe Acrobat AI Assistant
- Languages & Platforms for AI
- Cloud Computing & AI
- Planning for AI
o Building an AI-ready Organization
- Risks of AI
- Responsible/Ethical AI
- Image Generation & Diffusion Models
- (More) AI Tools
- Cybersecurity
o ML in Cybersecurity
o AWS Security Services
o Enterprise Security Platforms
- (Brief) History of AI