01:17:13

A Beginner's Guide to Artificial Neural Networks in Python with Keras and TensorFlow

20:06

Deep learning - explained simply | Early Stopping & Dropout

16:57

How do AI and neural networks work?

30:04

Convolutional Neural Network (CNN) – explained simply

18:04

Autoencoders - simply explained

14:23

PCA vs PCoA (Multidimensional scaling) - explained

22:01

SPSS for beginners in 22 minutes - enter data, plot and t-test

34:08

Recurrent neural network (RNN) - explained super simple

20:00

Stochastic gradient descent (SGD) vs mini-batch GD | iterations vs epochs - Explained

10:12

How to compute a p value and extract a critical value in R

15:03

Multinomial logistic regression | softmax regression | explained

20:21

Why we divide by n-1 when calculating the sample variance – the proof | unbiased estimator

12:27

Expected value vs mean

13:24

Bootstrap confidence intervals - explained

08:31

How to identify and deal with outliers | The 1.5 IQR rule | Boxplots

31:35

Bayesian statistics - the basics

17:09

How to check normal distribution | The normality assumption

14:09

PERMANOVA and permutation tests - explained

15:31

Statistical power - Parametric vs Nonparametric test

11:41

Meta-analysis | The inverse variance method | Forest plot in R

09:51

The Mantel-Haenszel method - clearly explained | deal with confounding

13:12

Understanding the odds ratio (OR) and the rare disease assumption | OR = RR?

10:09

Relative risk - how to calculate and interpret | 95% CI

05:27

Odds vs Probability - explained

16:49

The SIR model | the math of epidemics - explained with a simple example

26:20

Receptor ligand kinetics | mathematical modeling

13:08

How to build a system of differential equations (ODEs)

31:17

How to select a multivariate analysis or machine learning method

09:44

How to solve ordinary differential equations (ODEs) in R (deSolve)

10:20

Euler's method | Numerial methods

11:10

Understanding ordinary differential equations (ODE) - super simple example

13:36

The Cox proportional hazards model explained

12:56

Comparing Kaplan-Meier curves - the Log-rank test

19:29

Kaplan Meier curve – explained

12:48

Nonlinear mixed effects models (NLME) - explained

08:52

Nonlinear regression - how to fit a logistic growth model to data

05:09

Nonlinear regression - how to fit a dose-response curve in R

13:58

Nonlinear regression - comparing models with F test and AIC | parameter correlation

14:17

The Gauss Newton Method - explained with a simple example

10:55

Newton's method - explained | Newton-Raphson method | find roots and minimum value

16:46

Neural networks with continuous output | ANN vs Regression

18:16

Gradient descent - with a simple example

19:15

Nonlinear regression - the basics

20:38

Image classification with machine learning - explained | CNN ANN Logistic regression Decision trees

09:42

Understanding exponential decay and half-life with examples

11:13

Doubling time formulas with examples

14:56

Understanding exponential growth | discrete vs continuous growth

10:47

Why do we use Euler's number e?

12:40

Numerical differentiation - simply explained

26:14

Artificial neural networks (ANN) - explained super simple

12:10

Second derivative and partial derivative - the basics

08:58

Understanding derivatives - the basics

07:40

Understanding logarithms

13:34

Understanding the equation of a straight line | find equation | find line

16:35

Assumptions in Linear Regression - explained | residual analysis

08:19

Gaussian naive Bayes - explained with a simple example

12:55

Regression vs ANOVA and t-test

07:48

The weighted mean - explained

28:44

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

18:35

Lasso regression - explained