In this lecture series professor explains with instance based learning It is nothing but learning based on instances of data *Lazy Learning- Learn at last after creating a model - Knn Neighbour *Easy Learning - Learn as soon as we receive the data
- Started with storing things in DB and querying but what will happen when no such combinations are present
- Find the nearest closest value/similarity(distance)
- Use distance formula to calculate the nearest distances
- Manhattan are two such formulas for calculation of distances.
- If there are more nearby values then calculate the averages
- Curse of Dimensionality- More data more dimensions
- Locally Weighted Regression