Complete Machine Learning Course > Supervised Learning Algorithms

KNN in Machine Learning

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The parameter 'k' in KNN refers to the number of closest neighbours to include in the majority voting procedure.

The majority voting principle is used by the KNN classifier to identify the class of a data item. When the value of k is set to 5, the classes of the five nearest points are examined. The majority class is used to make predictions. Similarly, the mean value of the five nearest points is used in kNN regression.

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  1. Getting Started with Machine Learning
    4m 4s

  1. Introduction to Machine Learning
    13m 51s
  2. Environment Setup Part 1
    3m 32s
  3. Environment Setup Part 2
    4m 43s
  4. Environment Setup Part 3
    2m 34s

  1. Data Wrangling in Machine Learning
    12m 26s
  2. Importing Libraries and Dataset in Machine Learning
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  3. Handling Missing Data in Machine Learning
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  4. Handling Missing Data in Machine Learning - Practical
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  5. Encoding Categorical Data in Machine Learning
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  6. Encoding Categorical Data in Machine Learning - Practical
    8m 3s
  7. Splitting Dataset in Machine Learning
    3m 38s
  8. Splitting Dataset in Machine Learning - Practical
    9m 36s
  9. Normalizing the Data in Machine Learning - Part 1
    6m 19s
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  12. Exploratory Data Analysis in Machine Learning
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  15. Distribution Models in Machine Learning - Part 1
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  16. Distribution Models in Machine Learning - Part 2
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  17. Assignment : Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  2. Assignment : Machine Learning Paradigms
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  1. Overview of Classification
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  2. Logistic Regression in Machine Learning
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  3. Logistic Regression in Machine Learning - Practical - 1
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  4. Logistic Regression in Machine Learning - Practical - 2
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  5. KNN in Machine Learning
    7m 22s
  6. KNN in Machine Learning Practical - 1
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  7. KNN in Machine Learning - Practical 2
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  8. Decision Trees for Classification in Machine Learning
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  9. Decision Trees for Classification in Machine Learning - Practical - 1
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  10. Decision Trees for Classification in Machine Learning - Practical - 2
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  11. Decision Tree Iterative Dichotomiser in Machine Learning - 3
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  12. Random Forest in Machine Learning
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  13. Support Vector Machine Classifier
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  14. Support Vector Machine Classifier - Practical - 1
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  16. Naive Bayes Classifier in Machine Learning
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  17. Naive Bayes Classifier in Machine Learning Practical - 1
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  18. Naive Bayes Classifier in Machine Learning Practical - 2
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  19. Evaluating Classification Models Performance in Machine Learning
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  20. Evaluating Classification Models Performance in Machine Learning Practical
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  21. Assignment : Supervised Learning Algorithms
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  1. Simple Linear Regression in Machine Learning
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  2. Simple Linear Regression in Machine Learning - Practical
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  3. Salary Prediction using Linear Regression in Machine Learning
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  4. Multi-Linear Regression in Machine Learning
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  5. Startup Prediction using Multiple Regression in Machine Learning
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  6. Support Vector Regressor in Machine Learning
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  8. Support Vector Regressor in Machine Learning Practical - 2
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  9. Decision Tree Regressor in Machine Learning
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  10. Decision Tree Regressor in Machine Learning Practical - 1
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  11. Decision Tree Regressor in Machine Learning Practical - 2
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  12. Regressor Model Selection in Machine Learning
    9m 7s
  13. Evaluating Regression Model Performance in Machine Learning
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  14. Evaluating Regression Model Performance in Machine Learning - Practical
    11m 35s
  15. Assignment : Regression Algorithms
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  1. Distance Metrics in Machine Learning
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  2. K-Means Clustering in Machine Learning
    11m 3s
  3. K-Means Clustering in Machine Learning - Practical
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  4. Mall Customers Prediction using K Means Clustering in Machine Learning
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  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
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  6. Agglomerative Clustering in Machine Learning - Practical
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  8. DBscan Spatial Clustering in Machine Learning
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  10. Assignment : Unsupervised Learning Algorithms
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  1. Association Rule in Machine Learning - Apriori, FP Growth
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  4. FP Growth in Machine Learning
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  5. Market Basket Analysis using FP Growth in Machine Learning
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  6. Assignment : Association Rule Mining
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  2. Upper Confidence Bound in Machine Learning - Practical
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  3. Thompson Sampling in Machine Learning - Practical
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  4. Q Learning in Machine Learning
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  5. Assignment : Reinforcement Learning
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  1. Overview of Dimensionality Reduction in Machine Learning
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  1. Introduction to Keras and Deep Learning
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  1. Reinforcement Learning Project - Teach a Taxi Part 1
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  5. Loan Prediction Project Part 1
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  1. Course Summary
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  1. Interview Questions Part 1
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