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Course Outline

Introduction to Artificial Intelligence

  • The evolution of AI
  • Key definitions and industry terminology
  • Comparing AI with human intelligence
  • Emerging trends and future potential

Foundations of Machine Learning

  • Machine learning paradigms: supervised, unsupervised, and reinforcement learning
  • Essential ML algorithms
  • The ML lifecycle: from gathering data to evaluating models

Data Stewardship

  • Techniques for data acquisition
  • Cleaning and preprocessing data
  • Analyzing and visualizing data

Practical AI Applications

  • Case studies of real-world AI implementations
  • AI solutions tailored to specific industries
  • The role of AI in consumer-facing products

Ethical Frameworks

  • The impact of AI on employment
  • Addressing bias and ensuring fairness
  • Challenges related to privacy and security
  • The future landscape of AI ethics

Practical Lab Work

  • Programming tasks using Python
  • Data analysis projects utilizing real-world datasets
  • Building a basic ML model

Wrap-up and Path Forward

Requirements

  • A grasp of fundamental programming principles
  • Proficiency in Python programming
  • Knowledge of basic statistical and mathematical concepts

Target Audience

  • IT Professionals
 14 Hours

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