Unsupervised Machine Learning Series: Feature Engineering Methods
4h 42m + Hands-On Practice
Available in:
Unsupervised Machine Learning Series: Feature Engineering Methods
UML1 : UML161
This course discusses advanced techniques for transforming, reducing, selecting, and discovering structure in data without relying on target labels. Learners gain hands-on experience with a range of unsupervised feature engineering methods, including Principal Component Analysis (PCA), Robust PCA, Singular Value Decomposition (SVD), Autoencoders, Variable Clustering, and Non-Negative Matrix Factorization (NMF). The course also covers visualization of high-dimensional data using t-SNE and demonstrates how to deploy and score unsupervised learning models in real-world analytical workflows. Through practical demonstrations and real-world use cases, participants learn how to extract meaningful information from complex datasets and thereby improving model efficiency, uncover latent patterns, and build topic discovery and recommendation solutions.
The course focuses on the data width aspect of analytics, that is, techniques related to variables and feature engineering. To explore the data depth aspect, which focuses on observations and pattern discovery, consider taking the companion course Unsupervised Machine Learning Series: Pattern Discovery Methods.
The course focuses on the data width aspect of analytics, that is, techniques related to variables and feature engineering. To explore the data depth aspect, which focuses on observations and pattern discovery, consider taking the companion course Unsupervised Machine Learning Series: Pattern Discovery Methods.
Learn How To
- Explain the role of unsupervised learning in feature engineering and pattern discovery.
- Visualize and explore high-dimensional data.
- Reduce data dimensionality while preserving important information.
- Extract meaningful features from complex datasets.
- Identify and select relevant variables without the use of a target variable.
- Discover latent structures, patterns, and relationships within data.
- Deploy and score unsupervised learning models on new data.
- Interpret and evaluate feature engineering results to improve downstream analytics and decision-making.
Who Should Attend
- Data Scientists, Machine Learning Practitioners.
- Predictive Modelers and Analytics Professionals.
- Business Analysts working with large and complex datasets.
- AI/ML Engineers responsible for feature engineering and model deployment
- SAS Viya users seeking to apply unsupervised machine learning techniques
- Researchers and analysts interested in dimensionality reduction and latent structure discovery
Prerequisites
Before attending this course, you should have an understanding of elementary matrix algebra or linear algebra concepts and operations, including matrix dimensions, matrix addition and multiplication, matrix inversion, and scalars. You should have completed the Statistics You Need to Know for Machine Learning course or possess equivalent foundational knowledge of statistical and machine learning concepts. Experience working with tabular data and analytical workflows is helpful. Previous SAS programming knowledge is helpful but not required.
SAS Products Covered
SAS Viya
Course Outline
Unraveling Patterns Using Unsupervised Learning
- Introduction to unsupervised learning.
- t-SNE-based visualization of high-dimensional data.
- Principal component analysis.
- Robust principal component analysis.
- Singular value decomposition.
- Autoencoders.
- Scoring unsupervised learning models.
- Unsupervised selection method.
- Variable clustering.
- Non-negative matrix factorization (NMF).
- AI-assisted analytics with SAS Viya Copilot.
Live Class Schedule
Duration: 14 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
This course isn't publicly scheduled, but private training and coaching may be available. Contact us to explore options.
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Coaching Services
Take your training to the next level with personalized coaching. While private training offers structured coursework, coaching provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.