Unsupervised Learning Training in Sri Lanka

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. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in Sri Lanka. 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.

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.

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.