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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Shafi Akhtar
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Aniket Kumar prasad
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Very helpful and easy to understand all the concepts, best teacher for learning ML.

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Rishu Shrivastav
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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?

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VIKAS CHOUBEY
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very nicely explained

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Vrushali Kandesar
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Awesome and very nicely explained!!! One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)

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Mohd Mushraf
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Amazing Teaching

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Juboraj Juboraj
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Easy to understand & explain details.

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Joydeb
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Awesome Course sir and your teaching style is very GOOD.

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Shaga Chandrakanth Goud
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Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld

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Neel Khairnar
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Kushal is very good explainer he is covering all topics nicely 👍

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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
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    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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  5. Encoding Categorical Data in Machine Learning
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  7. Splitting Dataset in Machine Learning
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  9. Normalizing the Data in Machine Learning - Part 1
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  15. Distribution Models in Machine Learning - Part 1
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  17. Assignment : Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  1. Overview of Classification
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  2. Logistic Regression in Machine Learning
    4m 54s
  3. Logistic Regression in Machine Learning - Practical - 1
    32m 40s
  4. Logistic Regression in Machine Learning - Practical - 2
    12m 37s
  5. KNN in Machine Learning
    7m 22s
  6. KNN in Machine Learning Practical - 1
    35m 1s
  7. KNN in Machine Learning - Practical 2
    8m 46s
  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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  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
    1m 12s

  1. Simple Linear Regression in Machine Learning
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  3. Salary Prediction using Linear Regression in Machine Learning
    22m 42s
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    25m 13s
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    6m 48s
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    15m 46s
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    4m 53s
  10. Decision Tree Regressor in Machine Learning Practical - 1
    5m 2s
  11. Decision Tree Regressor in Machine Learning Practical - 2
    17m 10s
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    11m 35s
  15. Assignment : Regression Algorithms
    1m 12s

  1. Distance Metrics in Machine Learning
    6m 10s
  2. K-Means Clustering in Machine Learning
    11m 3s
  3. K-Means Clustering in Machine Learning - Practical
    9m 49s
  4. Mall Customers Prediction using K Means Clustering in Machine Learning
    18m 3s
  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
    4m 39s
  6. Agglomerative Clustering in Machine Learning - Practical
    4m 45s
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    14m 42s
  8. DBscan Spatial Clustering in Machine Learning
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    19m
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    2m 41s

  1. Association Rule in Machine Learning - Apriori, FP Growth
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    7m 15s
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  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
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    8m 41s
  3. Thompson Sampling in Machine Learning - Practical
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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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