Instance Based Learning

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

###knn * 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 * Euclidian * 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