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Section outline

    • Introduction to Machine Learning.
    • Supervised Learning vs. Unsupervised Learning.
    • Linear Regression Models (single and multiple variables).
    • Gradient Descent fundamentals.
    • Logistic Regression Models.
    • Challenges with Single Neurons.
    • Functioning of Multi-layered Neural Networks.
    • Differences between Machine Learning and Deep Learning.
    • Creating and training neural networks with TensorFlow and Keras.
    • Understanding Backpropagation.
    • Overview of Generative AI.
    • Introduction to Large Language Models (LLMs).
    • OpenAI and ChatGPT fundamentals.
    • Manual prompting for various tasks.
    • Chatbot creation with OpenAI.
    • Lab: Implementing OpenAI API for chatbot development and task automation.