## 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 1h

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

### Credit Card Customer Segmentation

For this project, we’ll play the role of a data scientist at a credit card company to segment customers into groups using K-means clustering in Python, allowing the company to tailor strategies for each segment.

## The Dataquest guarantee

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## Master skills faster with Dataquest

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