Hierarchical clustering is an alternative approach to k-means clustering for identifying groups in the dataset. It does not require us to pre-specify the number of clusters to be generated as is required by the k-means approach. Let's talk in more details.
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We are going to talk about linear regression, one of the most well known and well understood algorithms in machine learning. We are going to focus on the simple linear regression, which contains only one input variable. But the same logic and analyses will extend to the multi-variable linear regression. Let's talk in more details.
Gradient Boosting is another technique for performing supervised machine learning tasks, like classification and regression. XGBoost stands for Extreme Gradient Boosting; it is a specific implementation of the Gradient Boosting method which uses more accurate approximations to find the best tree model. It employs a number of nifty tricks that make it exceptionally successful, particularly with structured data. Let's talk in more details.