**COURSE**

# Machine Learning Fundamentals

Learn the fundamentals of machine learning using k-nearest neighbors.

This course will teach you the fundamentals of machine learning using k-nearest neighbors. This course also includes evaluating model error, hyperparameter optimization, cross validation and more.

By the end of this course, you'll be able to:

Course Info:

## Machine Learning Fundamentals

Beginner

The average completion time for this course is 10-hours.

This course requires a premium subscription. This course includes five paid missions and one guided project. It is the 17th course in the Data Scientist in Python path.

## Learn Machine Learning Fundamentals

### Introduction to K-Nearest Neighbors

Learn the basics of machine learning to suggest optimal AirBnB list prices.

### Evaluating Model Performance

Learn how to test models using error metrics and simple validation.

### Multivariate K-Nearest Neighbors

Improve your predictions by using more features.

### Hyperparameter Optimization

Vary the k value to improve performance.

### Cross Validation

Learn how to use k-fold cross validation to perform more rigorous testing.

### Predicting Car Prices

Practice the machine learning workflow using k-nearest neighbors to predict car prices.