Supervised and Unsupervised Learning8:03 with Nick Pettit
There are many different approaches or models in machine learning, but generally, they can be broken down into two major categories called supervised learning and unsupervised learning.
Vocabulary and Definitions
- Model: An algorithm or an approach to a problem
- Probability: A means of expressing how likely it is that an event will occur, or a way of measuring how close a value might be to the actual correct value
- Supervised learning: A case where a machine intelligence is tasked with predicting a category or a quantity
- Unsupervised learning: A case where a computer analyzes unlabeled data and has no previous examples, and tries to identify patterns in the data
- Classification: A supervised machine learning model that makes a prediction about how a piece of data should be categorized
- Regression: A supervised machine learning model that attempts to predict a quantity or a number
- Clustering: An unsupervised machine learning model that attempts to group similar examples together
- Ethical Design: Treehouse course - Stage 3, in particular, covers machine learning.
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