## Course overview

In this course, you’ll learn key concepts such as KNN Algorithms (K-Nearest Neighbors), error metrics including the Mean Squared Error and the Root Mean Squared Error and caret, a machine learning library for the R programming language. You’ll learn how to optimize machine learning algorithms for better accuracy and performance of trained models using hyperparameter optimization.

You’ll then dig into performing rigorous model testing using k-fold cross-validation. As you learn these new skills, you’ll be working with AirBnB prices data from Washington D.C. to predict the optimal price for generating profit from a Washington D.C. home rental.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll complete a project to predict car prices using the K-Nearest Neighbors algorithm. This project is a chance for you to combine the skills you learned in this course and practice a machine learning workflow.

## Key skills

- Understanding the key concepts of machine learning and common pitfalls
- Identifying a proper machine learning workflow
- Implementing the k-nearest neighbors algorithm
- Employing the caret library

## Course outline

### Introduction to Machine Learning in R [6 lessons]

### Introduction to Machine Learning Concepts 1h

Lesson Objectives- Establish a machine learning workflow
- Identify how the k-nearest neighbors algorithm works
- Define the role of Euclidean distance in the k-nearest neighbors algorithm

### Evaluating Model Performance 1h

Lesson Objectives- Employ the caret library to carry out the machine learning process
- Evaluate model performance using RMSE
- Compare RMSE values between different models

### Multivariate K-Nearest Neighbors 1h

Lesson Objectives- Incorporate multiple features into your k-nearest neighbor model
- Perform more involved data cleaning through normalization
- Employ piping to create easy-to-digest summaries of your model performance

### Cross Validation 1h

Lesson Objectives- Define holdout and k-fold cross-validation
- Analyze model performance using k-fold cross-validation
- Perform k-fold cross-validation in caret

### Hyperparameter Optimization 1h

Lesson Objectives- Identify how tuning model hyperparameters can affect the model performance
- Optimize for the best hyperparameter value using grid search
- Employ caret to perform hyperparameter optimization

### Guided Project: Predicting Car Prices 1h

Lesson Objectives- Implement the machine learning workflow on a new dataset
- Employ caret to carry out the entire machine learning workflow
- Explore making models

## Projects in this course

### Guided Project: Predicting Car Prices

For this project, we’ll step into the role of data scientists to predict car prices using the k-nearest neighbors algorithm and R, practicing the machine learning workflow on a real dataset.

## The Dataquest guarantee

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We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

## Master skills faster with Dataquest

### Go from zero to job-ready

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Build confidence with our in-depth projects, and show off your data skills.

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Work with real data from day one with interactive lessons and hands-on exercises.

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