Distance-based Models: Implementing Distance-based Algorithms
Math
| Intermediate
- 9 Videos | 1h 9m 14s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseanalyze the data used to implement a classification model using K Nearest Neighborsimplement a function that classifies a point using the K Nearest Neighbors algorithmclassify test data points using your own KNN classifier and evaluate the model using a variety of metricsimplement a function that uses KNN in order to perform regression
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obtain predictions on test data for your own implementation of a KNN regressorcode the individual steps involved in performing a clustering operation using the K-means algorithmdefine a function that clusters the points in a dataset using the K-means algorithm and then test itsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 25sUP NEXT
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2.Analyzing Data to be Classified11m 34s
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3.Building a KNN Classifier12m 14s
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4.Testing and Evaluating a KNN Classifier4m 28s
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5.Building a KNN Regressor11m 47s
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6.Testing and Evaluating a KNN Regressor5m 40s
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7.Computing Centroids and Clusters11m 44s
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8.Building and Evaluating a K-Means Model7m 28s
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9.Course Summary1m 56s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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