The Map of Statistics (all of Statistics in 15 mins!) - Upscaled using AI

MCMC - 5 - Hamiltonian Monte Carlo (HMC)

MCMC - 4 - Gibbs Sampling

MCMC - 3 - Random Walk MCMC (Metropolis-Hastings)

MCMC - 2 - Markov Chains

MCMC - 1 - Introduction to Markov Chain Monte Carlo

Conditional vs. Regular Logistic Regression (Theory + R Code)

TSA - ARIMA - Explore different models: AR(1), AR(2), MA(1), ARMA(1,1), etc.

TSA - ARIMA Introduction

GAM - Penalized Least Squares

GAM - Splines - Natural Cubic Spline, Smoothing Splines

GAM - Splines - Intro (Polynomials, Piecewise Polynomials, Splines)

GLM vs. GAM - Generalized Additive Models

Regression Diagnostics (2/2) - Generalized Linear Models - Residuals, QQ-plot, Outliers

Regression Diagnostics (1/2) - Linear Models - Residuals, QQ-plot, Outliers

GLM - Multinomial Regression (3/3) - Ordinal Data (Cumulative Link)

GLM - Multinomial Regression (2/3) - Nominal Data (Baseline Category)

GLM - Multinomial Regression (1/3) - Intro

Trees - Weights and Feature Importance (Theory + Code)

Accelerated Failure Time (AFT) vs. Cox Proportional Hazards (CoxPH)

Accelerated Failure Time (AFT)

2 Examples - Mixed vs. Regular Models

Cost Complexity Pruning (Theory + Code)

Build a Decision Tree from scratch using Python (numpy)

Decision Trees - Stop Criteria, Categorical Data, NA's, Implementation

Decision Trees - Split Criteria

Decision Trees

Quantile Regression - Numerical Solutions

Quantile Loss

Linear vs. Quantile Regression

R vs Python - 25 Coding Differences

Survival Analysis - Cox PH - Breslow Estimator

Survival Analysis - Cox PH - Partial Likelihood

Survival Analysis - Cox Proportional Hazards

Exploratory FA Code in R (psych)

CFA - Code Example in R (lavaan)

SEM - Code example in R (lavaan package)

SEM - Structural Equations Modelling

EFA vs. CFA - Exploratory vs. Confirmatory Factor Analysis

Factor Analysis - Statistical Intro 3 - Rotation, cov vs. cor, improper solution & the WOW criteria

Factor Analysis - Statistical Intro 2 - Estimation: PCA and ML

Factor Analysis - Statistical Intro 1 - Linear Model, Orthogonality

Clustering - Hopkins Statistic - Proof

Clustering - Hopkins Statistic - Definition and Code (Python)

NN - 26 - SGD Variants - Momentum, NAG, RMSprop, Adam, AdaMax, Nadam (NumPy Code)

NN - 25 - SGD Variants - Momentum, NAG, RMSprop, Adam, AdaMax, Nadam (Theory)

NN - 22 - Batch Normalization - Derivatives and Inference

NN - 21 - Batch Normalization - Theory

NN - 20 - Learning Rate Decay (with PyTorch code)

NN - 24 - Activations - Part 2: ReLU Variants

NN - 23 - Activations - Part 1: Sigmoid, Tanh & ReLU

NN - 10 - Cross Entropy and Softmax - Derivatives

NN - 9 - Cross Entropy and Softmax

NN - 19 - Weight Initialization 2 - What to do? Xavier Glorot & Kaiming He inits

NN - 18 - Weight Initialization 1 - What not to do?

NN - 17 - Dropout (Theory + @PyTorch code)

NN - 16 - L2 Regularization / Weight Decay (Theory + @PyTorch code)

NN - 15 - Generalization and the Bias-Variance tradeoff (PyTorch code)

NN - 14 - Generalization and the Bias-Variance tradeoff (Theory)

Variational Auto Encoder (VAE) - Theory