TileStats

0:16:57

How do AI and neural networks work?

0:11:40

Eigenvectors and eigenvalues - simply explained

0:22:11

PCA : the basics - explained super simple

0:18:36

How to choose an appropriate statistical test

0:14:22

Linear regression | the basics - for beginners

0:03:06

TileStats YouTube channel | Recommendation

0:11:27

Linear mixed effects models - the basics

0:14:01

Random forest classification - simply explained

0:14:59

Partial least squares regression (PLSR) - explained

0:20:22

PCA : the math - step-by-step with a simple example

0:20:25

Logistic regression : the basics - simply explained

0:11:01

Euclidean distance and the Mahalanobis distance (and the error ellipse)

0:34:08

Recurrent neural network (RNN) - explained super simple

0:30:04

Convolutional Neural Network (CNN) – explained simply

0:26:14

Artificial neural networks (ANN) - explained super simple

0:12:23

The Poisson distribution vs the normal distribution

0:09:14

Mean, median and mode

0:13:48

MLE vs OLS | Maximum likelihood vs least squares in linear regression

0:25:59

Regression Trees and the complexity parameter

0:13:40

PCA : how to interpret the weights/loadings and Varimax rotation

0:28:44

Support Vector Machines (SVM) - the basics | simply explained

0:10:54

k-means clustering - explained

0:24:26

Linear discriminant analysis (LDA) - simply explained

0:18:56

Validation techniques - explained with simple examples (Hold-out, cross-validation, LOOCV)