Browse all ML math lessons67 lessons across 7 modules
Module 1

Statistics

Explore 15 essential concepts in Statistics designed to build your machine learning mathematical foundation.

Basic Statistics

Foundational concepts including mean, median, mode, and variance.

Population vs Sample

Distinguish between the entire group and a subset of data.

Sampling Distributions

Probability distribution of a given statistic based on a random sample.

Central Limit Theorem

Why averages of samples tend to be normal, regardless of population distribution.

Confidence Intervals

Range of values derived from sample statistics that is likely to contain the population parameter.

Hypothesis Testing Fundamentals

Framework for making statistical decisions using experimental data.

P-Values & Significance

Evaluating the strength of evidence against the null hypothesis.

Type I vs Type II Errors

Understanding false positives and false negatives in testing.

One-Sample T-Test

Testing if a sample mean significantly differs from a known population mean.

A/B Testing

Compare two versions of a variable to determine which performs better.

ANOVA

Analysis of variance to check means across multiple groups.

Correlation vs Causation

Understanding the difference between related variables and cause-and-effect.

Resampling Methods

Techniques like bootstrapping and cross-validation for model validation.

Maximum Likelihood Estimation (MLE)

Method for estimating the parameters of a probablity distribution.

Bayesian vs Frequentist Approach

Contrasting philosophies on probability and statistical inference.