Applied Unsupervised Learning in Python
Completed by Alexander Marsh
May 16, 2026
30 hours (approximately)
Alexander Marsh's account is verified. Coursera certifies their successful completion of Applied Unsupervised Learning in Python
What you will learn
Apply unsupervised learning methods, such as dimensionality reduction, manifold learning, and density estimation, to transform and visualize data.
Understand, evaluate, optimize, and correctly apply clustering algorithms using hierarchical, partitioning, and density-based methods.
Use topic modeling to find important themes in text data and use word embeddings to analyze patterns in text data.
Manage missing data using supervised and unsupervised imputation methods, and use semi-supervised learning to work with partially-labeled datasets.
Skills you will gain
- Category: Data Quality
- Category: Python Programming
- Category: Model Evaluation
- Category: Exploratory Data Analysis
- Category: Embeddings
- Category: Unstructured Data
- Category: Machine Learning Methods
- Category: Unsupervised Learning
- Category: Data Transformation
- Category: Anomaly Detection
- Category: Applied Machine Learning
- Category: Data Preprocessing
