Python Data Science: Unsupervised Machine Learning

Python Data Science: Unsupervised Machine Learning
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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

What you’ll 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

Description
Who this course is for:
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

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