Все публикации

0:00:45

Homoscedasticity in Linear Regression | The cs Underdog

0:01:00

Independence of observations | The cs Underdog

0:00:57

Dependent and Independent variables | The cs Underdog

0:36:58

Multiclass Logistic Regression | Machine Learning Lecture 65 | The cs Underdog

0:00:46

Linearity in Linear Regression | The cs Underdog

0:00:31

Linear Regression or not? | The cs Underdog

0:12:24

Softmax Function | Machine Learning Lecture 64 | The cs Underdog

0:07:11

Logit Function | Machine Learning Lecture 63 | The cs Underdog

0:01:00

Best fitting curve using Machine Learning | The cs Underdog

0:01:00

How to write the Equation of a Curve to solve using ML? | The cs Underdog

0:37:11

Logistic Regression | Machine Learning Lecture 62 | The cs Underdog

0:00:31

How to generate points around a function with some noise? | The cs Underdog

0:00:57

Linear Regression can do Curve Fitting | The cs Underdog

0:01:00

Linear models can draw curves, not just straight lines | The cs Underdog

0:00:41

Finding equation of line in Machine Learning v/s traditional programming | The cs Underdog

0:11:48

Sigmoid Function | Machine Learning Lecture 61 | The cs Underdog

0:00:51

How is ML approach different from normal programming? | The cs Underdog

0:16:12

The Perceptron Algorithm | Machine Learning Lecture 60 | The cs Underdog

0:00:56

When to use ML? | The cs Underdog

0:33:50

Fisher's Linear Discriminant | Machine Learning Lecture 59 | The cs Underdog

0:01:00

Intuition behind Linear Regression | The cs Underdog

0:17:13

Eigenvalues and Eigenvectors | Machine Learning Lecture 58 | The cs Underdog

0:00:58

Use case of Linear Regression | The cs Underdog

0:18:00

Classification with Least Squares | Machine Learning Lecture 57 | The cs Underdog