ML - Supervised Learning
Ref: https://www.udemy.com/course/aws-ai-practitioner-certified/learn/lecture/44886601
- đź”§Â Learn a mapping function
- Can predict output for new unseen input data
- Needs labeled data
- very powerful
- difficult to perform on millions of datapoints
Regression Algorithms
- đź”§Â Predicts numeric value based on input data
- Continuous output variable
- đź’ˇLinear regression (straight line to describe relationship) very common, but many other types of regression
- Examples
- Predicting House Prices
- Stock Price Prediction
- Weather Forecasting
- Simple linear regression diagram
Classification Algorithms
- đź”§Â Predicts categorical label of input data
- Types and examples
- Binary Classification (e.g. classify emails as “spam” or “not spam”)
- Multi-class Classification (e.g. classify animals in a zoo as "mammal", "bird" or "reptile”)
- Multi-label Classification (e.g. assign multiple labels to a movie, like "action" and "comedy”)
- Example of a classification algorithm: kNN (k-Nearest Neighbors) → give a datapoint the label(s) of the k closest datapoints
- 💡 kNN can also do regression → give a datapoint the average values of the k closest datapoints
- Binary classification example diagram
Training, Validation, and Test Sets
- Training Set: trains the model
- Validation Set: validates model performance between training iterations
- âť—Â Validation sets are optional
- Test Set: evaluates final model performance (after all training has been done)