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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R
Rishu Shrivastav
5

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

V
VIKAS CHOUBEY
5

very nicely explained

V
Vrushali Kandesar
5

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)

M
Mohd Mushraf
5

Amazing Teaching

J
Juboraj Juboraj
5

Easy to understand & explain details.

J
Joydeb
5

Awesome Course sir and your teaching style is very GOOD.

S
Shaga Chandrakanth Goud
5

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

N
Neel Khairnar
5

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

S
Shaik Feroz
5

good

H
HANEEF
5

where do we get datasets for assignments

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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
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    9m 36s
  9. Normalizing the Data in Machine Learning - Part 1
    6m 19s
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    6m 58s
  11. Finding Machine Learning Datasets
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  12. Exploratory Data Analysis in Machine Learning
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  15. Distribution Models in Machine Learning - Part 1
    33m 55s
  16. Distribution Models in Machine Learning - Part 2
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  17. Assignment : Data Preprocessing for Machine Learning
    1m 40s

  1. Machine Learning Paradigms
    9m 55s
  2. Assignment : Machine Learning Paradigms
    3m 25s

  1. Overview of Classification
    5m 22s
  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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  10. Decision Trees for Classification in Machine Learning - Practical - 2
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  13. Support Vector Machine Classifier
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  14. Support Vector Machine Classifier - 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
    11m 8s
  19. Evaluating Classification Models Performance in Machine Learning
    7m 31s
  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
    8m 57s
  2. Simple Linear Regression in Machine Learning - Practical
    12m 34s
  3. Salary Prediction using Linear Regression in Machine Learning
    22m 42s
  4. Multi-Linear Regression in Machine Learning
    2m 53s
  5. Startup Prediction using Multiple Regression in Machine Learning
    25m 13s
  6. Support Vector Regressor in Machine Learning
    10m 45s
  7. Support Vector Regressor in Machine Learning - Practical - 1
    6m 48s
  8. Support Vector Regressor in Machine Learning Practical - 2
    15m 46s
  9. Decision Tree Regressor in Machine Learning
    4m 53s
  10. Decision Tree Regressor in Machine Learning Practical - 1
    5m 2s
  11. Decision Tree Regressor in Machine Learning Practical - 2
    17m 10s
  12. Regressor Model Selection in Machine Learning
    9m 7s
  13. Evaluating Regression Model Performance in Machine Learning
    8m 46s
  14. Evaluating Regression Model Performance in Machine Learning - Practical
    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
  7. Divisive Clustering in Machine Learning - Practical
    14m 42s
  8. DBscan Spatial Clustering in Machine Learning
    8m 50s
  9. Mall Customers Prediction using Hierarchical Clustering in Machine Learning
    19m
  10. Assignment : Unsupervised Learning Algorithms
    2m 41s

  1. Association Rule in Machine Learning - Apriori, FP Growth
    7m 19s
  2. Association Rule in Machine Learning - Apriori Practical
    7m 15s
  3. Market Basket Analysis using Apriori in Machine Learning
    17m 50s
  4. FP Growth in Machine Learning
    7m 43s
  5. Market Basket Analysis using FP Growth in Machine Learning
    11m 46s
  6. Assignment : Association Rule Mining
    46s

  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
    21m 4s
  2. Upper Confidence Bound in Machine Learning - Practical
    8m 41s
  3. Thompson Sampling in Machine Learning - Practical
    4m 18s
  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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  6. Variance and Bias in Machine Learning
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  1. Advance Trends 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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  3. Reinforcement Learning Project - Teach a Taxi Part 3
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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
    19m 22s
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  1. Career Guideline
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