Aspire     Essential Math for Data Science     Essential Math for Data Science Track 3: Math Behind ML Algorithms

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Knowing the math behind machine learning (ML) opens up many exciting avenues. There are vast amounts of ML algorithms you could learn. However, the distance-based algorithms K Nearest Neighbors and K-means clustering are arguably the most popular due to their simplicity and efficacy.
In this course, practice building a classification model using the K Nearest Neighbors algorithm. Build upon this algorithm to perform regression. Then, perform a clustering operation by implementing the K-means algorithm. And in doing so, explore the techniques involved in converging the centroids towards their optimal positions.
Upon completion, you'll be able to perform classification, regression, and clustering using the KNN and K-means algorithms.

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Objectives

Distance-based Models: Implementing Distance-based Algorithms discover the key concepts covered in this course analyze the data used to implement a classification model using K Nearest Neighbors implement a function that classifies a point using the K Nearest Neighbors algorithm classify test data points using your own KNN classifier and evaluate the model using a variety of metrics implement a function that uses KNN in order to perform regression obtain predictions on test data for your own implementation of a KNN regressor code the individual steps involved in performing a clustering operation using the K-means algorithm define a function that clusters the points in a dataset using the K-means algorithm and then test it summarize the key concepts covered in this course