Все публикации

K-means++ & Lloyd's0:02:51

K-means++ & Lloyd's algorithm

Principal Component Analysis0:03:31

Principal Component Analysis (PCA) - Step by Step

Decision Tree -0:06:51

Decision Tree - Gini Impurity

Radial Basis Function0:02:47

Radial Basis Function Kernel - Gaussian Kernel

Kernel Trick Visualization,0:04:21

Kernel Trick Visualization, Derivation, and Explanation.

Perceptron0:03:29

Perceptron

Soft Margin SVM0:04:20

Soft Margin SVM

Maximum Margin Classifier,0:04:00

Maximum Margin Classifier, SVM - Support Vector Machine

Logistic Regression |0:04:00

Logistic Regression | Binary Logistic Regression

Bayesian Inference &0:03:32

Bayesian Inference & Maximum a Posteriori Estimation | Bayesian Statistics

Linear Regression as0:02:09

Linear Regression as Maximum Likelihood

Maximum Likelihood Estimation0:02:56

Maximum Likelihood Estimation

Consistency in Estimators,0:03:14

Consistency in Estimators, Bias of Consistent Estimators

Bias & Variance0:02:28

Bias & Variance Tradeoff with MSE

Variance and Standard0:01:34

Variance and Standard Error of an Estimator/Statistic

Bias of an0:01:55

Bias of an Estimator

Point Estimators &0:01:31

Point Estimators & Function Estimators

K-fold Cross-Validation0:01:34

K-fold Cross-Validation

Parameters and Hyperparameters0:01:16

Parameters and Hyperparameters

Weight Decay -0:02:13

Weight Decay - L2 Regularization Example

No Free Lunch0:00:44

No Free Lunch Theorem (NFL)

K-Nearest Neighbor Regression0:02:23

K-Nearest Neighbor Regression

Overfitting And Underfitting0:02:27

Overfitting And Underfitting In Machine Learning

Linear Regression0:03:20

Linear Regression