Unsupervised Learning & Clustering: K-Means, Hierarchical, DBSCAN, GMM, PCA for Data Science & ML Mastery.
Sub Category
- Other IT & Software
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Objectives
- Understand the core principles and diverse applications of unsupervised learning in data science.
- Master K-Means clustering, including initialization strategies, optimization, and effective evaluation.
- Implement Hierarchical Clustering techniques (Agglomerative & Divisive) and interpret dendrograms for meaningful insights.
- Apply DBSCAN for density-based clustering, effectively identifying clusters of varying shapes and sizes, and detecting outliers.
Pre Requisites
- Intermediate Python programming skills (functions, data structures, basic libraries like NumPy and Pandas).
- Basic understanding of statistics (mean, median, standard deviation, variance).
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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