Course 5: Unsupervised Machine Learning

How can we uncover the hidden patterns in complex data sets? Dimension reduction techniques like PCA or t-SNE simplify multiple variables into easy-to-understand "maps," which help reveal relationships between them. This allows for better decision-making. This approach distinguishes scientific "clustering" from common sense "filtering," enabling the identification of market niches through data analysis profiling techniques.

This course will enable you to: 

• Understanding exploratory analysis

• Discovering hidden patterns within data sets

• Mastering all "pattern finding" algorithms used in AI applications

• Mapping complex data sets of multiple variables in simple charts

• Evaluating the quality of reduced multidimensionality solutions

• Differentiating between clustering and filtering

• Running professional segmentation with intelligent clustering

• Applying it all using SAS Viya and Python