Introduction Principal Component Analysis (PCA) is an unsupervised Machine Learning algorithm for dimensionality reduction. In Data Science and Machine Learning, large datasets with numerous features are often analyzed. PCA simplifies these complex datasets by retaining their essential information while reducing their dimensionality. It transforms a large set of correlated variables into a smaller set of uncorrelated variables known as principal components. These principal components capture the maximum variance in the data.

Introduction K-Means is an example of a clustering algorithm. Clustering is a fundamental concept in Machine Learning, where the goal is to group a set of objects so that objects in the same group are more similar to each other than to those in other groups. Clustering belongs to the set of unsupervised Machine Learning algorithms, that is no ground truth is needed. Among the various clustering algorithms, K-Means stands out for its simplicity and efficiency.

Introduction Gradient Boosting is an ensemble model of a sequential series of shallow Decision Trees. The single trees are weak learners with little predictive skill, but together, they form a strong learner with high predictive skill. For a more detailed explanation, please refer to the post Gradient Boosting for Regression - Explained. In this article, we will discuss different implementations of Gradient Boosting. The focus is to give a high-level overview of different implementations and discuss the differences.