Introduction to Unsupervised Machine Learning in Python

What's inside

4 lessons · 1 project

• 01
  Introduction to Unsupervised Machine Learning

  <p>In this lesson, we'll become familiar with the concept of unsupervised machine learning, as well as its differences from supervised machine learning, and when to use each of them. We'll also split the dataset into clusters, then use a scatter plot to visualize the results.</p>

  79 min
• 02
  Iterative K-means algorithm

  <p>In this lesson, we'll implement K number of clusters and the maximum number of iterations as parameters in the previously-built algorithm. We'll then create an iteration and understand how the algorithm should be able to stop it. Finally, we'll wrap all the code in a function capable of splitting any two-by-two DataFrame into any number of clusters we want.</p>

  64 min
• 03
  Number of Clusters and the Elbow Rule

  <p>In this lesson, we'll evaluate the grouping performed in the last lesson and learn how to measure the distance between the data points and how to calculate the model’s inertia from scratch. We'll then use the algorithm created in the previous lesson to understand how to plot, then interpret the elbow curve to find the best number of clusters.</p>

  33 min
• 04
  K-Means with Scikit-Learn and Interpreting Results

  <p>In this lesson, we'll explore the Scikit-Learn implementation of the K-Means Clustering algorithm and use it to create new clusters using all the variables in the dataset. After the cluster analysis, we’ll discuss how to analyze the results, determine the main differences between each cluster, and interpret the results.&nbsp;</p>

  69 min
• 05
  Guided Project: Credit Card Customer Segmentation Project

  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.

  52 min

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

Share the evidence of your hard work with your network and potential employers.