Course Content
Lesson 1 – Introduction to AI & ML Concepts (2 hours)
Topics: Definition of AI, ML, and Deep Learning Historical evolution and key milestones Types of machine learning: supervised, unsupervised, reinforcement learning AI applications in healthcare, finance, education, and more Hands-On: Exploring AI in everyday life through case examples Setting up Google Colab for coding exercises
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Lesson 1 – Introduction to AI & ML Concepts (2 hours)
Lesson 2 – Data Foundations & Preprocessing (3 hours)
Lesson 3 – Supervised Learning: Regression & Classification (4 hours)
Lesson 4 – Unsupervised Learning: Clustering & Dimensionality Reduction (3 hours)
Lesson 5 – Introduction to Neural Networks & Deep Learning (4 hours)
Lesson 6 – Model Deployment & Future Directions (2 hours)
1. Foundations of AI & Machine Learning

  • Topics:

    • Biological inspiration and artificial neurons

    • Architecture of a neural network: layers, weights, and activation functions

    • Forward and backward propagation

    • Overview of TensorFlow and Keras

  • Hands-On:

    • Building a simple feedforward neural network in TensorFlow

    • Training the network on an image classification dataset

    • Evaluating and visualizing performance

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