Module 04
AI & Machine Learning Basics
Understand AI Without the Hype
Learn core AI and machine learning concepts through small offline scikit-learn examples, model evaluation, overfitting, and responsible AI practice.
0 of 12 weeks complete0%
Learning Objectives
- Explain AI, machine learning, supervised learning, and key ML vocabulary in plain language
- Prepare small datasets and run simple scikit-learn workflows for regression or classification
- Interpret evaluation metrics, confusion matrices, and model limitations honestly
- Recognize overfitting, underfitting, and basic strategies for improving model quality
- Identify bias, privacy, and fairness risks in real-world AI systems
Glossary
- Artificial Intelligence (AI)
- Technology that performs tasks associated with human intelligence, such as pattern recognition or prediction.
- Machine Learning
- A branch of AI where models learn patterns from data instead of only following fixed rules.
- Feature
- An input value or column used by a model to make a prediction.
- Label
- The correct answer a supervised learning model is trained to predict.
- Overfitting
- When a model learns the training data too closely and performs poorly on new data.