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.


  • 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


  • 2m 25s
  • 11m 34s
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    3.  Building a KNN Classifier
    12m 14s
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    4.  Testing and Evaluating a KNN Classifier
    4m 28s
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    5.  Building a KNN Regressor
    11m 47s
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    6.  Testing and Evaluating a KNN Regressor
    5m 40s
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    7.  Computing Centroids and Clusters
    11m 44s
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    8.  Building and Evaluating a K-Means Model
    7m 28s
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    9.  Course Summary
    1m 56s


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