Module 2: Machine Learning Basics
Understanding the core mechanisms that power AI systems
Executive Summary
- Machine learning (ML) is the engine of AI—algorithms learn patterns from data to make predictions.
- Three core ML paradigms: supervised, unsupervised, and reinforcement learning.
- Data quality and quantity directly impact model performance; prioritize clean, relevant datasets.
Key Concepts
If AI is the broader concept, then Machine Learning (ML) is the engine that powers it. ML involves algorithms learning from data to make predictions or identify patterns without being explicitly programmed. There are three main types:
- Supervised learning (learning from labeled data)
- Unsupervised learning (finding structure in unlabeled data)
- Reinforcement learning (learning through feedback and iteration)
Executives should understand that ML thrives on data quality and quantity. The better your data, the better your model.
Interactive Charts
This scatterplot illustrates how supervised models work. Toggle the trend line to see how a machine learning model would fit the data.
This visualization demonstrates unsupervised clustering with dynamic color-coded customer segments based on spending behavior and engagement.
Real-World Examples
Email Spam Filters
ML models train on labeled examples of spam emails to identify and filter out new spam messages.
Fraud Detection
Banks use ML to detect anomalies in transaction patterns that might indicate fraudulent activity.
Predictive Maintenance
Manufacturers use sensor data to predict equipment failure before it happens, reducing downtime.
Customer Segmentation
E-commerce platforms group shoppers based on behavior patterns for targeted marketing campaigns.
Discussion Prompts
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What data sources do you already collect? Are they being used to their full potential?
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Which business question could ML help answer in your organization?
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Where would you need human oversight to ensure the reliability of an ML model?
Knowledge Check: True or False
Question 1: In supervised learning, the algorithm learns from labeled training data to make predictions or decisions.
Question 2: Feature engineering is unnecessary when using deep learning models.
Question 3: Overfitting occurs when a model performs well on training data but poorly on unseen data.
Call to Action
Pick one dataset in your business and brainstorm with a data-savvy colleague how ML might unlock value from it.