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Training Course on Artificial Intelligence

Course Contents

Day 1: Understanding Artificial Intelligence

  1. Introduction to AI
    • Definition and brief history of AI
    • Importance and applications of AI in various fields
  2. Types of AI
    • Narrow AI vs. General AI
    • Examples of AI applications in real life
  3. Machine Learning Basics
    • What is Machine Learning (ML)?
    • Supervised, Unsupervised, and Reinforcement Learning
    • Basic algorithms: Linear Regression, Logistic Regression, and Decision Trees
  4. Deep Learning
    • Introduction to Neural Networks
    • Structure of a neural network: neurons, layers, and activation functions
    • Popular deep learning architectures: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

Day 2: Practical Applications and Ethical Considerations

  1. AI in Practice
    • Real-world applications of AI: Image recognition, Natural Language Processing (NLP), Autonomous vehicles, etc.
    • Case studies and examples of successful AI implementations
  2. Data for AI
    • Importance of data in AI applications
    • Data preprocessing: cleaning, normalization, and feature engineering
  3. Introduction to Tools and Frameworks
    • Popular AI frameworks: TensorFlow, PyTorch, and scikit-learn
    • Introduction to Python programming language for AI development
  4. Ethical Considerations in AI
    • Bias and fairness in AI algorithms
    • Privacy and security concerns
    • Responsible AI development and deployment practices
  5. Future of AI
    • Emerging trends and advancements in AI technology
    • Potential impacts of AI on society and the workforce

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