8:04
P-Values Explained
DataMListic
4:36
Covariance and Correlation Explained
4:18
Model Calibration - Brier Score Explained
4:47
Bias-Variance Trade-off - Explained
6:21
Why We Divide by N-1 in the Sample Variance (The Bessel's Correction)
3:21
Kullback-Leibler (KL) Divergence Mathematics Explained
2:21
Why We Don't Accept The Null Hypothesis
11:34
Why Minimizing the Negative Log Likelihood (NLL) Is Equivalent to Minimizing the KL-Divergence
3:15
Spearman Correlation Explained in 3 Minutes
Hyperparameters Tuning: Grid Search vs Random Search
3:38
Cross-Validation Explained
5:40
Singular Value Decomposition (SVD) Explained
4:49
Least Squares vs Maximum Likelihood
Marginal, Joint and Conditional Probabilities Explained
3:33
Covariance Matrix - Explained
9:12
T-Test Explained
8:01
Basic Probability Distributions Explained: Bernoulli, Binomial, Categorical, Multinomial
5:36
Poisson Distribution - Explained
4:04
L1 vs L2 Regularization
3:45
Z-Test Explained
4:24
Confidence Intervals Explained
4:11
Overfitting vs Underfitting - Explained
2:37
Why L1 Regularization Produces Sparse Weights (Geometric Intuition)
Content-Based Recommendations - Recommender Systems Part 1
4:27
Cross-Entropy - Explained
8:07
The Curse of Dimensionality
9:03
The Kernel Trick
4:22
RBF Kernel Explained: Mapping Data to Infinite Dimensions
8:15
Bayesian Optimization
9:33
Gaussian Processes
5:44
An Introduction to Graph Neural Networks
5:53
Introduction to HMMs | Hidden Markov Models Part 1
8:02
t-SNE - Explained
8:21
Student's t-Distribution - Explained
5:35
Variational Inference - Explained
5:31
Gamma Function - Explained
5:27
Degrees of Freedom - Explained
4:41
Central Limit Theorem - Explained
4:42
Lagrange Multipliers - Explained
4:13
Monte Carlo Simulation - Explained
