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.
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