AI Apps with Unsupervised Machine Learning

It is common to have data sets with multiple variables describing business topics. But how can we extract all the hidden patterns from such complex data sets? Reducing the number of variables into simple "maps" with PCA becomes essential to highlight invisible relations within the data, facilitating the correct actions to take. This workshop also explains the difference between scientific market "clustering" and simple common sense "filtering".

It empowers the definition of market niches and profiles them using Data Analysis techniques. Additionally, the program covers illustrations that reveal associations between the components of multiple variables for efficient tracking of pattern evolution. The workshop also includes practical applications with two different technologies, allowing participants to become more like consultants than mere experts.

Workshop Overview

Learning Outcomes

• Highlighting the role of statistical data analysis in Unsupervised Machine Learning.

• Discovering hidden patterns within data sets.

• Understanding the "Dimension Reducibility" concept.

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

• Mapping complex data sets of multiple variables in simple charts.

• Visualizing relationships between variables and categories.

• Evaluating the quality of reduced multidimensionality.

• Differentiating between clustering and filtering.

• Delving into all clustering techniques and their applications

• Running professional segmentation with clustering.

• Applying it all using specialized software.

Detailed Course Schedule

• Day 1:

  • Matrix Factorization

  • Principal Component Analysis

• Day 2:

  • t-SNE and Multi-Dimensional Scaling

  • Simple Correspondence Analysis

• Day 3:

  • Agglomerative Clustering

  • K Means and Medoids

• Day 4:

  • Recommender Systems

• Day 5:

  • Gaussian Mixture Models

• Colored PPT documents / Videos.

• Multidimensional Reducibility

• Hierarchical vs. Divisive Clustering

• Proprietary vs. Open-Source tools solutions

• Eigenvalues and Eigenvectors

• Team Exercises

• The science behind mapping illustration.

What will it be about?