Jessica Ko

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Jessica Ko

Machine Learning Engineer @Twitter

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

Guided Project: 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

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.

Money

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

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 your project portfolio

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

Challenge yourself with exercises

Challenge yourself with exercises

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

Showcase your path certification

Showcase your path certification

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

Grow your career with
Dataquest.

98%
of learners recommend
Dataquest for career advancement
4.85
Dataquest rating
SwitchUp Best Bootcamps
$30k
Average salary boost
for learners who complete a path
Aaron

Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessi

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

Join 1M+ data learners on
Dataquest.

1

Create a free account

2

Choose a learning path

3

Complete exercises and projects

4

Advance your career

Start learning today