Applied Unsupervised Learning in Python

About this course

In “Applied Unsupervised Learning in Python,” you will learn how to use algorithms to find interesting structure in datasets. You will practice applying, interpreting, and refining unsupervised machine learning models to solve a diverse set of problems on real-world datasets. This course will show you how to explore unlabelled data using several techniques: dimensionality reduction and manifold learning for condensing and visualizing high-dimensional data, clustering to reveal interesting groups and outliers, topic modeling for summarizing important themes in text, methods for dealing with missing data, and more. This course also covers best practices associated with different techniques, as well as demonstrating how unsupervised learning can be used to improve supervised prediction. This is the second course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.

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.