Applied Unsupervised Learning in Python

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

• Apply unsupervised methods like dimensionality reduction and manifold learning
• Evaluate and optimize clustering algorithms
• Use topic modeling to analyze text data themes
• Manage missing data with supervised and unsupervised imputation
• Employ semi-supervised learning for partially labeled datasets

• Prior hands-on experience with core machine learning concepts
• Ability to independently apply main tools and methods