We learn what Unsupervised Learning algorithms are, working of the algorithms and their scope of application – Clustering and Dimensionality Reduction.
K-means clustering is a popular unsupervised learning algorithm to resolve the clustering problems in Machine Learning or Data Science. In this module, you will learn how the algorithm works and later implement it.
Hierarchical Clustering is an ML technique or algorithm to build a hierarchy or tree-like structure of clusters. For example, it is used to combine a list of unlabeled datasets into a cluster in the hierarchical structure. This module will teach you the working and implementation of this algorithm.
High-dimensional Clustering is the clustering of datasets by gathering thousands of dimensions.
Principal Component Analysis for Dimensional Reduction is a technique to reduce the complexity of a model like eliminating the number of input variables for a predictive model to avoid overfitting. Dimension Reduction-PCA is a well-known technique in Python for ML, and you will learn everything about this method in this module.