Course Outline

Introduction to AI

  • History of AI
  • Definitions and terminology
  • AI vs. human intelligence
  • Future trends and potential

Machine Learning Basics

  • Types of machine learning: supervised, unsupervised, reinforcement
  • Key ML algorithms
  • ML workflow: from data collection to model evaluation

Data Management

  • Data collection techniques
  • Data cleaning and preprocessing
  • Data analysis and visualization

AI in Practice

  • Case studies of AI applications
  • Industry-specific AI solutions
  • AI in consumer products

Ethical Considerations

  • AI and job displacement
  • Bias and fairness in AI
  • Privacy and security issues
  • Future of AI ethics

Lab Project

  • Python programming assignments
  • Data analysis projects using real-world datasets
  • Development of a simple ML model

Summary and Next Steps

Requirements

  • An understanding of basic programming concepts
  • Experience with Python programming
  • Familiarity with basic statistics and mathematics

Audience

  • IT Professionals
 14 Hours

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