Our Trainings
Unsupervised ML Capstone
Synopsis
This training program is designed to immerse participants in the world of unsupervised machine learning, focusing on the exploration and analysis of data without predefined labels. This course will delve into key unsupervised learning techniques such as clustering, dimensionality reduction, and anomaly detection. Participants will engage in practical exercises and projects, allowing them to apply these techniques to diverse datasets and uncover hidden patterns and structures. The training culminates in a capstone project where participants will demonstrate their ability to analyze and interpret complex data using unsupervised learning methods, preparing them to address real-world challenges in various domains.
Objectives
Provide participants with a deep understanding of unsupervised learning principles, including clustering, dimensionality reduction, and anomaly detection techniques. 2Offer hands-on experience with popular unsupervised learning algorithms and tools, such as K-means, hierarchical clustering, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding). Teach participants how to effectively explore and visualize data to uncover hidden patterns, structures, and insights, enhancing their data analysis skills. Enable participants to apply their knowledge by completing a capstone project, where they will analyze a real-world dataset using unsupervised learning techniques, interpret the results, and present their findings in a comprehensive and professional manner.
Methodology
Ice Breaking, Presentation, Hands-on Lab Activity, Brainstorming, Discussion And Q&A.
