Machine Learning can be broadly categorized into supervised and unsupervised learning. But what’s the difference, and when should you use each?
In this blog, we’ll cover:
- Supervised Learning: The model learns from labeled data (e.g., classification, regression).
- Unsupervised Learning: The model finds patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Real-world examples of both approaches.
Here’s a quick comparison:
- Supervised Learning: Predicting house prices based on historical data.
- Unsupervised Learning: Grouping customers based on purchasing behavior.
Understanding these concepts is crucial for choosing the right ML approach for your problem. Stay tuned for more advanced topics!
