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:

    • Importance of data in ML

    • Data collection, formats, and sources

    • Data cleaning: handling missing values, outliers

    • Data normalization and feature scaling

    • Splitting datasets into training, validation, and test sets

  • Hands-On:

    • Importing datasets into Google Colab

    • Using Pandas and NumPy for cleaning and preprocessing

    • Normalizing and splitting a sample dataset

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