22 тысяч подписчиков
170 видео
Understanding derivatives - the basics
Support Vector Machines (SVM) - the basics | simply explained
Artificial neural networks (ANN) - explained super simple
Logistic regression : the basics - simply explained
Linear regression | the R-squared value
K nearest neighbors (KNN) - explained | validation
Assumptions in Linear Regression - explained | residual analysis
Validation techniques - explained with simple examples (Hold-out, cross-validation, LOOCV)
Paired vs unpaired t-test
Bootstrap confidence intervals - explained
Bayesian statistics - the basics
Statistical power - Parametric vs Nonparametric test
How to identify and deal with outliers | The 1.5 IQR rule | Boxplots
Linear regression | hypothesis testing
Nonlinear mixed effects models (NLME) - explained
The basics of type 1 and 2 errors | explained with a simple example
Kaplan Meier curve – explained
PERMANOVA and permutation tests - explained
The unpaired t-test | Independent samples t-test
Canonical correlation analysis - explained
Understanding the odds ratio (OR) and the rare disease assumption | OR = RR?
Covariance and the correlation matrix - with simple examples
The repeated-measures ANOVA | explained with a simple example
Euclidean distance and the Mahalanobis distance (and the error ellipse)
A Beginner's Guide to Artificial Neural Networks in Python with Keras and TensorFlow
How to compute a p value and extract a critical value in R
How to choose an appropriate statistical test
Hierarchical clustering - explained
Eigenvectors and eigenvalues - simply explained
Transfer learning - explained (VGG16, MobileNet, ResNet, EfficientNet)
Zero-inflated Poisson (ZIP) regression
Learn Python from Scratch for Data Analysis & Machine Learning – linear regression and t-test
Second derivative and partial derivative - the basics
Why do we use Euler's number e?
U-Net clearly explained | Image Segmentation with AI
Newton's method - explained | Newton-Raphson method | find roots and minimum value
One-way ANOVA: the basics
Linear discriminant analysis (LDA) - simply explained
Linear mixed effects models - the basics
Nonlinear regression - how to fit a dose-response curve in R
MANOVA - explained with a simple example
Gaussian naive Bayes - explained with a simple example
t-test VS confidence intervals
Numerical differentiation - simply explained
Post hoc test | Tukey's test and Dunnett's test – Explained
The geometric mean
Nonlinear regression - how to fit a logistic growth model to data
PCA vs PCoA (Multidimensional scaling) - explained
Random forest classification - simply explained
The probability of making a type 2 error | explained with a simple example (part 1/2)
Multiple linear regression - explained with two simple examples
