Introduction
Artificial Intelligence (AI) has rapidly evolved into a transformative force across industries, enabling machines to perform tasks that previously required human intelligence. This 5-day course provides a structured and practical introduction to the core concepts, terminology, and techniques used in modern AI systems. Participants will explore the foundations of machine learning, neural networks, data processing, and real-world AI applications. Through guided discussions and hands-on exercises, learners will gain an understanding of how AI systems are built, deployed, and evaluated. By the end of the course, attendees will be prepared to explore more specialized or advanced areas of AI.
Course Objectives
- Understand the fundamental concepts and history of Artificial Intelligence.
- Learn essential terminology used in AI, machine learning, and deep learning.
- Gain practical insight into how common AI models are developed and trained.
- Explore real-world applications and use cases across industries.
- Build confidence in evaluating AI systems and identifying ethical considerations.
Target Audience
- Students or professionals with little to moderate technical background.
- Business leaders who want to understand AI capabilities and limitations.
- Analysts or developers beginning their AI learning journey.
- Educators and researchers entering the field of applied AI.
- Anyone curious about how AI systems work and how they are used today.
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: Introduction to AI Concepts• History and evolution of Artificial Intelligence
• Types of AI: Narrow, General, Superintelligence
• Key terminology: algorithms, models, datasets, training
• Overview of AI applications across industries
• Understanding the AI development lifecycle0 - Day 2: Machine Learning Fundamentals• Types of ML: supervised, unsupervised, reinforcement learning
• Training vs. testing datasets
• Evaluating model accuracy and performance
• Feature engineering basics
• Hands-on: Build a simple classifier0 - Day 3: Deep Learning & Neural Networks• Structure of artificial neural networks
• Activation functions and optimization
• Introduction to CNNs and RNNs
• Overfitting, underfitting, and regularization
• Hands-on: Train a simple neural network0 - Day 4: Data, Tools & Practical AI• Data preprocessing for AI projects
• Overview of AI tools (TensorFlow, PyTorch, scikit-learn)
• Understanding GPUs vs. CPUs
• Deployment basics: APIs, cloud AI platforms
• Hands-on: Experiment with an AI framework0 - Day 5: Ethics, Trends & Capstone• Ethical considerations: fairness, bias, transparency
• AI governance & regulatory trends
• Emerging technologies: generative AI, multimodal AI
• Capstone activity: Designing a simple AI solution
• Final reflections and next steps for deeper learning0
Instructor
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