Introduction to Artificial Intelligence (AI) for Beginners -

About Course

Artificial Intelligence (AI) is no longer a concept of the future—it’s an essential part of our daily lives. From smart assistants and recommendation engines to language translation and self-driving cars, AI is reshaping the way we live, work, and learn.

This beginner-friendly course is designed to provide a gentle yet engaging introduction to the world of AI. Whether you’re a teacher, student, professional, or simply curious about technology, this course will help you understand how AI works, where it is applied, and why it matters.

Over the span of 20 hours, participants will explore the foundational concepts of AI, differentiate between Artificial Intelligence, Machine Learning, and Deep Learning, and experiment with practical tools that require no coding. As the course progresses, learners will also be introduced to Python basics, allowing them to train a simple AI model and understand how data fuels intelligent systems.

With hands-on activities, real-world examples, and interactive projects, this course is not just about learning theory—it’s about seeing AI in action. By the end, participants will gain confidence in understanding and applying basic AI concepts and will be ready to explore more advanced topics or integrate AI into their field of work or study.

Understand the core concepts and terminology of Artificial Intelligence
Differentiate between AI, Machine Learning, and Deep Learning
Identify real-world applications of AI across various industries
Explore how data powers intelligent systems
Use AI tools like Google Teachable Machine and ChatGPT without writing code
Write and execute basic Python code for AI model development
Train and evaluate a simple machine learning model
Understand the ethical implications and challenges of AI
Apply your knowledge in a capstone project and present your results

Course Content

✅ Module 1: What is AI?
📝 Description: This module introduces the concept of Artificial Intelligence in a simple and engaging manner. It explains what AI is, its importance, and how it is already a part of our lives. 📚 Topics Covered: Definition of AI History and evolution of AI AI in the real world (e.g., ChatGPT, Google Maps, Netflix recommendations) Common myths and misconceptions about AI 🛠️ Activities: Group discussion on how AI is used in daily life Watch an introductory video on AI Quiz on AI myths and facts

✅ Module 2: AI vs ML vs DL
📝 Description: This module clarifies the differences between Artificial Intelligence, Machine Learning, and Deep Learning. Participants will understand how these concepts are related and where each is used. 📚 Topics Covered: Definitions of AI, Machine Learning, and Deep Learning Differences and relationships among them Examples from real-world applications 🛠️ Activities: Visual comparison chart exercise Sorting examples into AI, ML, and DL categories Mini-case study walkthrough

✅ Module 3: Applications of AI in Daily Life
📝 Description: This module highlights how AI is transforming different industries and our everyday experiences, emphasizing practical and relatable use cases. 📚 Topics Covered: AI in education, healthcare, business, transport, etc. AI tools we use every day How AI is impacting jobs and careers 🛠️ Activities: Brainstorm AI applications in participants’ professions Interactive slideshow on AI across sectors Class discussion on AI's benefits and risks

✅ Module 4: Basics of Data for AI
📝 Description: Since data is the backbone of AI, this module introduces participants to the role of data in AI systems, how it is used, and why it's important. 📚 Topics Covered: What is data? Structured vs unstructured data Labeled vs unlabeled data How data is collected and prepared for AI 🛠️ Activities: Explore and sort a small dataset Identify types of data in given examples Hands-on task: load and visualize a dataset in Excel

✅ Module 5: Introduction to Machine Learning
📝 Description: Participants will get a simple, visual understanding of how machine learning works, including supervised and unsupervised learning and a model's life cycle. 📚 Topics Covered: What is machine learning? Types: Supervised, Unsupervised, Reinforcement Learning Steps: Data → Training → Model → Prediction → Evaluation 🛠️ Activities: Watch animated video on ML lifecycle Use Google Teachable Machine to train a no-code model Worksheet: Match ML types to real-world examples

✅ Module 6: AI Tools Without Coding
📝 Description: This module introduces beginner-friendly platforms that allow users to create AI models without programming knowledge, making AI accessible to all. 📚 Topics Covered: Google Teachable Machine Canva Magic Studio, ChatGPT, AutoML tools AI use-cases through drag-and-drop platforms 🛠️ Activities: Create an image or sound classifier with Teachable Machine Try writing prompts in ChatGPT Explore Canva Magic features for creative AI tools

✅ Module 7: Fundamentals of Python for AI
📝 Description: Python is the most widely used language in AI. This module provides a gentle introduction to Python programming concepts essential for AI applications. 📚 Topics Covered: Variables, data types, loops, functions Data handling using NumPy and Pandas Writing and running simple code in Google Colab 🛠️ Activities: Code snippets in Python (e.g., sum numbers, sort lists) Mini project: load a CSV file using Pandas Quiz on basic Python syntax

✅ Module 8: Build Your First AI Model
📝 Description: In this hands-on module, participants build their first basic machine learning model using Python. They will explore the process from data loading to prediction. 📚 Topics Covered: Importing datasets Splitting data into training and testing sets Using scikit-learn for training Evaluating performance (accuracy, confusion matrix) 🛠️ Activities: Use a simple dataset (e.g., Iris or student scores) Train a classification model Visualize results using matplotlib

✅ Module 9: Ethics, Bias & Future of AI
📝 Description: AI isn't just about technology—it impacts people. This module explores the ethical challenges AI poses and discusses the future role of AI in society. 📚 Topics Covered: AI bias and fairness Privacy concerns Deepfakes and misinformation Future trends in AI 🛠️ Activities: Group debate on AI ethics Analyze biased datasets and model outcomes Watch video on ethical AI fails and discuss

✅ Module 10: Capstone Mini Project & Presentation
📝 Description: The final module allows learners to apply everything they've learned by completing a small AI project and presenting their model, results, and reflections. 📚 Topics Covered: Choosing a dataset and defining a problem Training and evaluating a model Presenting findings in a clear and simple way 🛠️ Activities: Individual or group project Prepare slides or a short report Present to peers or class for feedback

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