Artificial Intelligence and Machine Learning Basics

This course is designed to introduce beginners and professionals from various industries to the transformative fields of Artificial Intelligence (AI) and Machine Learning (ML). It provides a foundational understanding of core concepts, hands-on experience in developing a basic machine learning model, and a discussion of ethical considerations in AI. By the end of this course, learners will gain the confidence to explore and apply AI and ML in their respective domains.

Learning Objectives (LO):

1. Understand Core AI and ML Concepts:

   • Learn the definitions, differences, and key components of AI and ML.
   • Understand fundamental algorithms, such as supervised and unsupervised learning, and how they function.
   • Explore real-world applications of AI and ML across various industries.

2. Develop a Basic Machine Learning Model Using Python:

   • Gain familiarity with Python libraries like NumPy, Pandas, and scikit-learn.
   • Learn to clean, preprocess, and analyze data for machine learning.
   • Build and evaluate a simple machine learning model using step-by-step guidance.

3. Learn Ethical Considerations in AI Applications:

   • Understand issues like bias, fairness, transparency, and accountability in AI systems.
   • Explore privacy concerns and the importance of secure data handling.
   • Discuss how to ensure ethical use of AI in real-world scenarios.

Target Audience:

• Beginners in technology seeking to enter the AI and ML domain.
• Professionals from non-technical backgrounds exploring AI for industry-specific applications.
• Anyone with an interest in learning about the potential and challenges of AI.

Prerequisites:

• Basic understanding of computers and programming concepts (helpful but not mandatory).
• A willingness to learn and explore.

Course Structure:

1. Introduction to AI and ML

   • What is AI?
   • Evolution and key milestones in AI and ML.
   • Applications in healthcare, finance, retail, and more.

2. Getting Started with Python for Machine Learning

   • Installing Python and setting up the environment.
   • Introduction to essential Python libraries.
   • Hands-on: Building a simple ML model.

3. Ethical and Social Implications of AI

   • Responsible AI practices.
   • Challenges and opportunities in adopting AI.