Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Unsupervised Machine Learning
Course
Learn how to distill messy data into meaningful groups with unsupervised machine learning! By the end of this course, you’ll know how to perform cluster analyses and dimensionality reduction using Python’s powerful machine-learning package, scikit-learn.
Estimated time
1 month
Enroll by
June 14, 2023
Get access to classroom immediately on enrollment
Skills acquired
Independent Component Analysis, Gaussian Mixture Models
What You Will Learn
Unsupervised Learning
1 month to completeUnsupervised learning gives you the power to find common patterns within data without knowing what to look for, and its application is virtually limitless — from market segmentation to computer vision to fraud detection. In this course, you’ll learn a multitude of techniques, including hierarchical and density-based clustering, gaussian mixture models, cluster validation, principal component analysis (PCA), and independent component analysis (ICA). In addition, you’ll apply what you’ve learned to identify customer segments within complex demographic data for a mail-order sales company.
Prerequisite knowledge
Intermediate Python, basic understanding of Machine Learning and Supervised Learning concepts.
Clustering
Learn the basics of clustering data and cluster data with the K-means algorithm.
Hierarchical and Density-Based Clustering
Cluster data with Single Linkage Clustering and DBSCAN, a clustering method that captures the insight that clusters are dense group of points.
Gaussian Mixture Models
Cluster data with Gaussian Mixture Models and optimize Gaussian Mixture Models with Expectation Maximization.
Dimensionality Reduction
Reduce the dimensionality of the data using Principal Component Analysis and Independent Component Analysis.
Course Project: Creating Customer Segments
In this project, you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.
All Our Courses Include
Real-time support
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Workspaces
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Course offerings
Class content
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Student community
- Real-time support
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
- Experienced Project Reviewers
- Real-Time Support
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Learn with the best.
Learn with the best.
Josh Bernhard
Data Scientist at Nerd WalletJosh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
Jay Alammar
InstructorJay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
Unsupervised Learning
Get started today
Learn
How to cluster and decompose data at will with Python’s powerful machine-learning library, scikit-learn.
Average Time
On average, successful students take 1 month to complete this program.
Benefits include
- Real-world projects from industry experts
- Real-time support
Program Details
Do I need to apply? What are the admission criteria?
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
What are the prerequisites for enrollment?
In order to succeed in this program, we recommend having:
- Experience in Intermediate Python.
- Basic understanding of machine learning concepts such as model training and validating.
- Basic understanding of supervised learning concepts such as labels, prediction, forecasting, and linear regression.
- Basic mathematics and statistics knowledge to perform simple probability calculations.
How is this course structured?
The Unsupservised Learning course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 15 hours.
The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
How long is this course?
Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
Can I switch my start date? Can I get a refund?
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
What software and versions will I need in this course?
You will need a computer running a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.x and supporting packages. Most modern Windows, OS X, and Linux laptops or desktop will work well; we do not recommend a tablet since they typically have less computing power. We will provide you with instructions to install the required software packages.