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
0/6
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:

    • Overview of supervised learning

    • Linear regression theory and implementation

    • Logistic regression for classification problems

    • Decision trees and random forests

    • Evaluation metrics: accuracy, precision, recall, F1-score

  • Hands-On:

    • Building a regression model in Scikit-learn

    • Training a decision tree on a classification dataset

    • Comparing models using performance metrics

Previous
Next
Register / Login to purchase courses
Scroll to Top