8 docs tagged with "statistics"

Introduction to the fundamental pillars of statistics in ML: Populations vs. Samples, Descriptive vs. Inferential statistics, and Data Types.

An intuitive introduction to probability theory, sample spaces, events, and the fundamental axioms that govern uncertainty in Machine Learning.

Exploring the essential plots and charts used in statistical analysis to identify patterns, distributions, and outliers in Machine Learning datasets.

Mastering measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation, range) to summarize and understand data distributions.

Understanding how to make predictions and inferences about populations using samples, hypothesis testing, and p-values.

A deep dive into Probability Mass Functions (PMF) for discrete data and Probability Density Functions (PDF) for continuous data.

Mastering high-level statistical plotting: visualizing distributions, regressions, and categorical relationships.

A deep dive into the Normal Distribution, the Central Limit Theorem, and why Gaussian assumptions are the backbone of many Machine Learning algorithms.