Introduction to AI & Machine Learning (ML)

• Overview of AI and its real-world applications
• Key concepts of supervised, unsupervised, and reinforcement learning
• Understanding data collection and preprocessing techniques

  Building AI Models

  • Introduction to popular AI libraries and frameworks like TensorFlow and scikit-learn
  • Building simple machine learning models (e.g., linear regression, decision trees)
  • Hands-on practice with AI model training, evaluation, and optimization

    Deep Learning Fundamentals

    • Basics of neural networks and deep learning
    • Introduction to convolutional neural networks (CNNs) for image processing
    • Building and training deep learning models using TensorFlow

      AI in Action: Real-World Projects

      • Applying AI techniques to solve real-world problems
      • Hands-on project where participants build a small AI model (e.g., image classifier, recommendation system)

        Introduction to Natural Language Processing (NLP)

        • Overview of NLP and its applications (e.g., chatbots, sentiment analysis)
        • Implementing basic NLP tasks using Python libraries like NLTK and spaCy