🤖Mathematics and Statistics for AI and Machine Learning

Why You Must Understand Data Before Modeling It

18 min

Mean, Median, Mode: The Three Ways to Find "The Middle"

19 min

Variance & Standard Deviation: How Spread Out Is Your Data?

16 min

Percentiles & Quartiles: Slicing Data Into Pieces

12 min

Skewness & Kurtosis: When Data Leans and When It Spikes

15 min

Visualizing Stats: Histograms, Box Plots, and Spotting Outliers

13 min

What Probability Really Is, and Why Prediction Needs It

14 min

Basic Probability Rules: AND, OR, NOT

15 min

Conditional Probability: How New Information Changes the Odds

17 min

Independence: When Events Don't Care About Each Other

16 min

Bayes' Theorem: Updating Your Beliefs with Data

14 min

Expected Value: What to Expect in the Long Run

17 min

What a Distribution Is, and Why Real-World Data Follows Patterns

17 min

Normal Distribution: The Famous Bell Curve

13 min

Bernoulli & Binomial: Yes/No Events and Counting Successes

14 min

Poisson Distribution: Counting Arrivals and Rare Events

15 min

Uniform Distribution: When Everything Is Equally Likely

19 min

Central Limit Theorem: The Most Magical Result in Statistics

15 min

Sampling and Sampling Distributions: Learning from a Slice

17 min

Confidence Intervals: How Sure Are We, Really?

19 min

Hypothesis Testing: The Logic of Proving Things with Data

19 min

p-values and Significance: What They Mean and What They Don't

16 min

# t-tests and Chi-square Tests: The Tests You'll Actually Use

17 min

A/B Testing: Inference in the Real World

19 min

Vectors and Vector Operations: Your Features Have a Shape

15 min

Matrices and Matrix Multiplication: Your Dataset Is a Matrix

17 min

Linear Transformations: How Matrices Move and Stretch Space

15 min

Eigenvalues and Eigenvectors: Finding the Directions That Matter

17 min

Matrix Decomposition: SVD and PCA Explained Simply

14 min

Where This Shows Up in ML: Neural Nets, PCA, and Recommendations

16 min

Derivatives and Gradients: The Slope Tells You Where to Go

16 min

Partial Derivatives: Changing One Thing at a Time

19 min

The Chain Rule, and Why It Powers Backpropagation

16 min

Gradient Descent Intuition: Rolling Downhill to the Answer

14 min

Learning Rates, Convexity, and Why Models Get Stuck

17 min

Local Minima and How to Escape Them

14 min

Maximum Likelihood Estimation: Picking the Most Believable Model

18 min

Bayesian Inference Basics: Prior, Likelihood, Posterior

19 min

Confident vs Guessing: Uncertainty in Model Predictions

19 min

Probabilistic Graphical Models: A Friendly Intro

16 min

Information Theory Basics: Entropy and Surprise

19 min

Reading ML Papers: Recognizing the Math in the Wild

18 min