Business Intelligence and Analytics - Dimension Reduction in Python - Nonlinear Methods
Master Data Mastery: Transform, Analyze, and Visualize! Dive into the world of Big Data, Governance, Python Analytics, Machine Learning, and AI with Stephanie Powers. Unlock data's power and elevate your expertise in modern analytics and data engineering. Enroll now!
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31 learners
What you'll learn
Understand and apply data governance principles to manage data effectively.
Analyze data types and structures using Python for data engineering tasks.
Create dashboards and visualizations in Python to present analytical insights.
Implement machine learning models in Python for classification and prediction tasks.
This course includes
56.5 hours of video
Certificate of completion
Access on mobile and TV
Summary
Full Transcript
This video applies dimension reduction using non-linear methods (including Manifold Learning with ISOMAP, Manifold Learning with Local Linear Embedding (LLE), and T-distributed Stochastic Neighbor Embedding (tSNE)) to image recognition models using KNN classification.
Python workbook available here: https://drstephpowers.github.io/BIA/
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