5 Topics
5 Days
17 Sessions
Programme Overview
Training Description
Business Leaders and Managers
Non-Technical Professionals from any industry (e.g., Marketing, HR, Finance)
IT Staff and Technology Enthusiasts (seeking foundational knowledge)
Career Changers and Recent Graduates
Data Enthusiasts and Project Managers
Educators and Policy Makers
Entrepreneurs and Small Business Owners
Session Objectives
- Understand the core terminology and general principles of Artificial Intelligence. Explain the basic process of Machine Learning (ML) and its contribution to AI. Identify the benefits and different types of AI. Recognize the ethical and societal considerations and risks of AI. Describe the use of AI in various industries and its future directions. Develop skills in prompt engineering for effective interaction with generative AI tools. Identify opportunities for AI adoption and governance activities in an organization.
About the Course
The Artificial Intelligence Essentials course offers a fundamental introduction to AI, designed for both technical and non-technical learners. It demystifies key concepts like Machine Learning, Neural Networks, and Generative AI, providing practical skills like prompt engineering to use AI tools effectively in the workplace. The training explores various real-world applications across different industries, emphasizing the ethical, legal, and societal considerations of AI adoption, including addressing potential biases and responsible use of data. The goal is to build AI literacy, enabling participants to confidently collaborate with and strategically evaluate AI opportunities within their organizations.
Curriculum & Topics
5 Topics | 17 Sessions
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Workshop 1.1:
Definition & History of AI: Differentiating Narrow AI, General AI, and Superintelligence.
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Workshop 1.2:
Key AI Terminology: Clarifying concepts like Machine Learning (ML) and Deep Learning (DL).
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Workshop 1.3:
Modern Applications: Examples of AI transforming industries like healthcare, finance, and logistics.
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Workshop 2.1:
Machine Learning (ML): Brief overview of supervised, unsupervised, and reinforcement learning.
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Workshop 2.2:
Generative AI (GenAI): Focus on models that create new content (text, images, code).
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Workshop 2.3:
AI Technologies: Introduction to Natural Language Processing (NLP) and Computer Vision.
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Workshop 3.1:
Role of Data: Understanding why high-quality data is the essential fuel for AI models.
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Workshop 3.2:
Data Lifecycle: Steps from data collection and preparation to model training and deployment.
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Workshop 3.3:
Data Bias: Recognizing and mitigating different types of bias in training data.
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Workshop 4.1:
Ethical Considerations: Discussing fairness, transparency, and accountability in AI systems.
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Workshop 4.2:
Algorithmic Bias: Understanding how systems can perpetuate or amplify human biases.
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Workshop 4.3:
Legal & Privacy Issues: Overview of data protection regulations relevant to AI implementation.
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Workshop 5.1:
AI Readiness Assessment: Tools to evaluate an organization's preparedness for adopting AI.
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Workshop 5.2:
AI Governance: Establishing frameworks for managing and overseeing AI projects responsibly.
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Workshop 5.3:
Practical Tools & Prompting: Hands-on introduction to using GenAI tools and techniques for effective instruction.
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Workshop 5.4:
Object Recognition
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Workshop 5.5:
Real Genetic Algorithm Examples to Optimize Business Processes
