Dimension Reduction: From Modeling To Visualization




Dimension reduction: From modeling to visualization

5 March 2019


Added 01-Jan-1970

As developers, engineers, scientists or students if we analyze data or want to learn how to analyze data, we need to have the required skills. We need to learn those skills helping us to improve our status in our current job, get a better job and be successful in next interviews we will have. Dimension reduction is among the subjects necessary to know if you want to be successful or become successful as developers, scientists or engineers.

In this workshop, you will learn about widely used dimension reduction methods such as PCA, ICA, t-SNE, and UMAP. You will learn how to explore them to reduce dimensionality of your data in Python, how to use methods like PCA in supervised learning schemes and how to improve your reports using visualization techniques like t- SNE. This will be a hands on workshop in which we will work on multiple datasets and will learn how to implement each one of the aforementioned methods on them in Python