Unsupervised Learning Training in San Francisco

Unsupervised Learning Training in San Francisco, USA

This course is designed to introduce participants to the key concepts, algorithms, and techniques used in unsupervised machine learning.

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Unsupervised Learning Training is a professional training program delivered by ProgNXT in San Francisco, USA. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Unsupervised Learning Training course with hands-on, instructor-led sessions. This training on Unsupervised Learning is designed to introduce participants to the key concepts, algorithms, and techniques used in unsupervised...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: DQA54
14 Hrs
Last Updated: April 2026

Course Rating5/5

This training on Unsupervised Learning is designed to introduce participants to the key concepts, algorithms, and techniques used in unsupervised machine learning. Unlike supervised learning, unsupervised learning works with unlabeled data and is used to uncover hidden patterns, structures, or relationships in the data. The course will cover important algorithms such as Clustering (e.g., K-Means, DBSCAN), Dimensionality Reduction (e.g., PCA, t-SNE), and Association Rule Learning. Participants will gain hands-on experience using Python and popular libraries like scikit-learn to solve real-world problems using unsupervised learning.

Welcome to the official Unsupervised Learning Training certification program in San Francisco, USA. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

Basic knowledge of Python programming.
A fundamental understanding of machine learning concepts (including supervised learning).
Familiarity with basic mathematics (e.g., linear algebra, probability, and statistics) is beneficial but not required.
Previous experience with data analysis and data preprocessing is helpful.

By the end of this course, participants will:

Understand the fundamental concepts of unsupervised learning and how it differs from supervised learning.
Be able to apply Clustering algorithms (e.g., K-Means, DBSCAN) and Dimensionality Reduction techniques (e.g., PCA, t-SNE) to real-world datasets.
Understand the principles of Association Rule Learning and implement Apriori and Eclat algorithms.
Gain experience in data preprocessing, feature extraction, and evaluation techniques specific to unsupervised learning.
Learn how to interpret and visualize the results of unsupervised learning algorithms.
Develop the skills to use unsupervised learning in anomaly detection, market basket analysis, and customer segmentation.

Upon completion of the course, participants will be able to:

Understand the differences between supervised and unsupervised learning.
Implement Clustering algorithms (e.g., K-Means, DBSCAN, Hierarchical Clustering) for data segmentation.
Apply Dimensionality Reduction techniques (e.g., Principal Component Analysis (PCA), t-SNE) for feature selection and visualization.
Perform Association Rule Mining using algorithms like Apriori to find relationships between variables in large datasets.
Evaluate the performance and effectiveness of unsupervised learning algorithms.
Use scikit-learn and Python libraries to solve unsupervised learning problems.

Participants will be assessed on the following topics:

Understanding and applying Clustering algorithms (e.g., K-Means, DBSCAN).
Effective use of Dimensionality Reduction techniques (e.g., PCA, t-SNE).
Implementing Association Rule Mining (e.g., Apriori algorithm) for market basket analysis.
Conducting data preprocessing and handling unlabeled data.
Ability to interpret results and evaluate model performance using appropriate metrics.
Solving real-world unsupervised learning problems with Python.

Upon successful completion of the course, participants will receive a ProgNXT Certificate of Completion, demonstrating their proficiency in Unsupervised Learning and its application to solving real-world problems.

Introduction to Unsupervised Learning and Clustering

Session 1: Introduction to Unsupervised Learning
- What is unsupervised learning?
- Key differences between supervised and unsupervised learning.
- Applications of unsupervised learning: Clustering, Dimensionality Reduction, Anomaly Detection, etc.
- Overview of scikit-learn and Python libraries for unsupervised learning.
Session 2: Data Preprocessing and Feature Engineering
- Understanding the need for data preprocessing in unsupervised learning.
- Handling missing data, feature scaling, and encoding categorical data.
- Feature extraction and feature selection.
Session 3: Clustering Algorithms
- Introduction to Clustering: Types of clustering (partitional vs hierarchical).
- K-Means Clustering: Algorithm, distance metric, and number of clusters.
- DBSCAN: Density-based clustering and its advantages.
Session 4: Hierarchical Clustering
- Introduction to Hierarchical Clustering: Agglomerative vs Divisive clustering.
- Dendrograms and determining the optimal number of clusters.

Dimensionality Reduction, Association Rule Learning, and Applications

Session 5: Dimensionality Reduction Techniques
- Introduction to Dimensionality Reduction and its significance.
- Principal Component Analysis (PCA): Understanding eigenvalues, eigenvectors, and the covariance matrix.
- t-SNE: Techniques for visualizing high-dimensional data in 2D or 3D.
Session 6: Association Rule Learning
- Introduction to Association Rule Mining and its applications (e.g., market basket analysis).
- The Apriori Algorithm and Eclat Algorithm for mining frequent itemsets.
- Evaluation metrics: Support, Confidence, Lift.
Session 7: Unsupervised Learning for Anomaly Detection
- Overview of Anomaly Detection: Identifying outliers or anomalies in data.
- Using K-Means and DBSCAN for anomaly detection.
Session 8: Putting It All Together: Final Evaluation
- Participants work on a final project to solve a real-world unsupervised learning problem.
- Final evaluation based on Clustering, Dimensionality Reduction, and Association Rule Mining techniques.
- Q&A and feedback.

This training on Unsupervised Learning provides participants with the tools and techniques necessary to perform tasks like Clustering, Dimensionality Reduction, and Association Rule Mining. By combining theory with hands-on practice, participants will leave with the skills needed to apply unsupervised learning algorithms to solve complex problems in data analysis.

Participants will receive:

Course slides with detailed explanations and illustrations.
Code examples and Jupyter notebooks for hands-on practice.
Real-world datasets for exercises, including customer data and transactional data for analysis.
Links to additional resources, research papers, and tutorials for further learning.
Assignments to reinforce the application of unsupervised learning concepts.
Access to scikit-learn documentation and tutorials for unsupervised learning techniques.

Participants will be evaluated through a hands-on project and a final quiz.
ProgNXT Certificate of completion will be awarded to participants who successfully complete the assessments.

Unsupervised Learning Training Events in san-francisco

Online Events

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

24th Oct 2026→25th Oct 2026

📍 San Francisco, USA

28th Oct 2026→29th Oct 2026

November 2026

📍 San Francisco, USA

3rd Nov 2026→4th Nov 2026

🌐 Online Instructor-Led

10th Nov 2026→11th Nov 2026

📍 San Francisco, USA

18th Nov 2026→19th Nov 2026

🌐 Online Instructor-Led

25th Nov 2026→26th Nov 2026

December 2026

🌐 Online Instructor-Led

7th Dec 2026→8th Dec 2026

📍 San Francisco, USA

12th Dec 2026→13th Dec 2026

📍 San Francisco, USA

19th Dec 2026→20th Dec 2026

🌐 Online Instructor-Led

19th Dec 2026→20th Dec 2026

Events for other courses in San Francisco, USA

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Unsupervised Learning Training in San Francisco