KNN Introduction
K Nearest Neighbors Introduction
K Nearest Neighbors
K Nearest Neighbors (KNN)
less than a minute
This course includes
4 articles
8 mins reading time
4 videos • 1 hr 7 mins
Course content
4 sections • 23 topics • Read: 8 mins • Video: 1 hr 7 mins
KNN Introduction
Issues with Linear/Logistic Regression
K Nearest Neighbors
KNN Algorithm
Time & Space Complexity
Introduction to KNN | K-Nearest Neighbors Algorithm | Explained with Examples
KNN Optimizations
Optimizations
Standardization
Distance-Weighted KNN
Mahalanobis Distance
Runtime Issue
Advanced Optimizations
KNN Optimizations | Standardization | Distance Weighted KNN | Mahalanobis Distance | Explained
Curse Of Dimensionality
Euclidean Distance
Issues with High Dimensional Data
Distance Concentration
Data Sparsity
Exponential Sample Requirement
Solution
Cosine Similarity
Normalization
Curse of Dimensionality | Cosine Similarity | Normalization | Explained with Examples
Bias Variance Tradeoff
KNN Dataset
High Bias, Low Variance
High Variance, Low Bias
‘K' Hyper-Parameter Tuning
Over-Fitting Vs Under-Fitting
K Nearest Neighbors (KNN) Bias-Variance Tradeoff | Explained with Examples
KNN Optimizations
KNN Optimizations
Curse Of Dimensionality
Curse Of Dimensionality
Bias Variance Tradeoff
Bias Variance Tradeoff