Topic area: Modeling
We shall study about linearly separable and inseparable datasets. We shall then then apply various clustering algorithms to these datasets. This is a hands-on workshop where the attendees will be using our online learning platform, refactored.ai to execute code on their laptops.
Linearly Separability and Inseparability are characteristic to various datasets we encounter in real life. In 2-dimensions and even sometimes in 3-dimensions, data visualization comes in handy where we can detect the nature of the data. In higher dimensions, this would be impractical which begs the need for alternative methods. We will showcase various experiments that will provide us hints to the nature of the data. Without prior knowledge of the datasets, clustering the datasets will not yield us the best results. We shall learn to work with real world examples as well as generate synthetic data to illustrate the efficacy of the algorithms. To understand which algorithms would fail and which ones are best equipped, we shall apply various clustering algorithms to these datasets. This includes clustering methods such as K-Means, DBScan, Spectral Clustering, Gaussian Mixture Models & Agglomerative clustering. We shall also study how these algorithms work and why some of these would outperform others.
This is a hands-on workshop where the attendees will be using our online learning platform, refactored.ai to execute code on their laptops.