Applied Unsupervised Learning with R

Learn Python for data science & machine learning, and build unsupervised learning models w/ a top Python instructor!

Created by: Maven Analytics • 1,500,000 Learners
Empowering everyday people with life-changing data skills

Created by: Alice Zhao
Data Science Instructor at Maven Analytics

What Will I Learn?

• Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders
• Prepare data for modeling by applying feature engineering, selection, and scaling
• Fit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCAN
• Use unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detection
• Apply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNE
• Build recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD)

Requirements

• We strongly recommend taking our Data Prep & EDA course before this one
• Jupyter Notebooks (free download, we'll walk through the install)
• Familiarity with base Python and Pandas is recommended, but not required

Target audience

• Data scientists who want to learn how to build and interpret unsupervised learning models in Python
• Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role
• Anyone interested in learning one of the most popular open source programming languages in the world