## Course overview

In this course, you’ll learn the fundamentals of the k-means algorithm and how to use it to build a model to segment data. You’ll also learn to work with clusters with activities such as finding the optimal number of clusters, creating new clusters using the k-means algorithm in scikit-learn, and interpreting the results from a k-means model.

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 combine your new skills to complete a project to perform a credit card customer segmentation.

## Key skills

- Identifying applications of unsupervised machine learning
- Implementing a basic k-means algorithm
- Evaluating and optimizing the performance of a k-means model
- Building a k-means model using scikit-learn

## Course outline

### Introduction to Unsupervised Machine Learning in Python [5 lessons]

### Introduction to Unsupervised Machine Learning 2h

Lesson Objectives- Explain the concept of unsupervised machine learning
- Identify the different types of unsupervised machine learning
- Identify the differences between unsupervised and supervised machine learning and when to use each one
- Implement a basic clustering algorithm from scratch
- Visualize clusters

### Iterative K-means algorithm 2h

Lesson Objectives- Implement the k-means algorithm from scratch
- Implement the algorithm using K as a parameter
- Control the number of iterations of the algorithm
- Run the algorithm for different combinations of variables and different numbers of clusters

### Number of Clusters and the Elbow Rule 2h

Lesson Objectives- Calculate model inertia
- Visualize the elbow rule
- Find the optimal number of clusters

### K-Means with Scikit-Learn and Interpreting Results 2h

Lesson Objectives- Create new clusters using the K-Means algorithm in scikit-learn
- Interpret the results from a K-Means model
- Determine the main differences between each cluster

### Guided Project: Credit Card Customer Segmentation 2h

Lesson Objectives- Prepare the data for modeling
- Standardize the data
- Choose the best number of clusters
- Perform the clusterization
- Understand and explain the outcomes

## Projects in this course

### Guided Project: Credit Card Customer Segmentation

For this project, we’ll build a clustering model to segment credit card customers into different groups in order to apply different solutions for each type of customer.

## The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

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

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

### Build your project portfolio

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

### Challenge yourself with exercises

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

### Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.