STAT 185 - Introduction to Unsupervised Learning Learning Course

[STAT 185 - Introduction to Unsupervised Learning Learning Course] STAT 185 - Introduction to Unsupervised Learning STAT 185 introduces students to unsupervised learning techniques used to discover patterns and structures in unlabeled data. The course covers topics such as clustering algorithms (k-means, hierarchical clustering), dimensionality reduction (principal component analysis, t-distributed stochastic neighbor embedding), and anomaly detection. Emphasis is placed on understanding the principles of unsupervised learning, evaluating clustering performance, and interpreting data clusters. Through practical applications and case studies, students learn how to preprocess data, apply clustering algorithms, visualize data clusters, and extract meaningful insights from large datasets. STAT 185 prepares students for careers in data mining, pattern recognition, and research involving exploratory data analysis and unsupervised learning methods.This Learning Course is a platform that helps students to study and learn. The platform provides all the required material for the students to study. The material includes Study guide, Exam Questions & Answers, Study Notes and other resources that are useful for the students. It also gives information about the upcoming exams so that students can prepare themselves accordingly.