Machine Learning Fundamentals K-Nearest Neighbors

About

Learn key probability terms, apply set operations and probability rules, and solve problems through analytical methods and simulations.

Learning Objectives

You will be able to:

Define experiment, outcome, event, and sample space.
Calculate the union and intersection of sets.
Apply three probability rules.
Solve probability problems using simulations.

Content

Topic	Skills
Slides	- Define experiment, outcome, event, and sample space - Calculate the union and intersection of sets.
Probability	- Apply three probability rules. - Solve probability problems using simulations.

Prerequisites

Write and run Python code in a Jupyter notebook.
Create basic Python functions.
Import and use pandas and numpy.
Create data visualizations in a noteboook using Matplotlib and Seaborn.

References

📖 Reference Materials

Important note

Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)