Josh Starmer - The StatQuest illustrated guide to machine learning!!!

Activation Function AUC Backpropagation Bias-Variance Tradeoff Biases Binomial Distribution Branch (Decision Tree) Confusion Matrix Continuous Data Continuous Probability Distributions Cost Function Data Leakage Dependent Variables Discrete Data Discrete Probability Distributions Exponential Distribution False Positive False Positive Rate Feature Gaussian (Normal) Distribution Gini Impurity Hidden Layer Histograms Hypothesis Testing Impure Independent Variables Internal Node (Decision Tree) Layers (Neural Networks) Leaf (Decision Tree) Learning Rate Likelihood vs Probability Loss Function Margin Mean Squared Error (MSE) Mini-Batch Stochastic Gradient Descent Models Nodes (Neural Networks) Normal (Gaussian) Distribution Null Hypothesis Overfitting p-values Parameter Poisson Distribution Polynomial Kernel Probability Distributions Probability vs Likelihood Precision Precision Recall Graph R2 (R-squared) Radial Kernel Recall ReLU Activation Function Residual ROC Root Node (Decision Tree) Sensitivity Sigmoid Activation Function Soft Margin SoftPlus Activation Function Specificity Stochastic Gradient Descent Sum of Squared Residuals (SSR) Support Vector Support Vector Classifier Tangent Line Testing Data Training Data True Positive Rate Underflow Uniform Distribution Weights

The StatQuest Illustrated Guide to Machine Learning!!! By Josh Starmer, Ph.D. TRIPLE BAM!!!