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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Suresh Kumar
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Hi Sir, I want a clearity up on these 1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.

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Ayush Bharti
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how can i download the finaldata.csv?

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Jagannath Mahato
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Hello Kushal Sir! Your way of teaching is very good. I thank you from my heart ❤️ that you are providing such good content for free.

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Muhammad Qasim
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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world. Extremely helpful and I recommend to my peer as well for this course.

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Shafi Akhtar
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None

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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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  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
    11m 19s
  3. Handling Missing Data in Machine Learning
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  4. Handling Missing Data in Machine Learning - Practical
    17m 3s
  5. Encoding Categorical Data in Machine Learning
    6m 34s
  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
  10. Normalizing the Data in Machine Learning - Part 2
    6m 58s
  11. Finding Machine Learning Datasets
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  12. Exploratory Data Analysis in Machine Learning
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  13. Plotting Graphs in Machine Learning - Part 1
    19m 12s
  14. Plotting Graphs in Machine Learning - Part 2
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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
    1m 40s

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

  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
    32m 40s
  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
    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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  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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  15. Support Vector Machine Classifier - Practical - 2
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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
    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
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  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
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  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
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  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
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  4. FP Growth in Machine Learning
    7m 43s
  5. Market Basket Analysis using FP Growth in Machine Learning
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  6. Assignment : Association Rule Mining
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  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
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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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  2. Principal Component Analysis in Machine Learning
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  3. Principal Component Analysis in Machine Learning - Practical
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  4. Linear Discriminant Analysis in Machine Learning
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  6. Assignment : Dimensionality Reduction
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  1. Basics of Regularization and Optimization in Machine Learning
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  2. Cross Validation in Machine Learning
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  3. Hyperparameter Tuning in Machine Learning
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  4. Sampling Methods in Machine Learning
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  5. Underfitting and Overfitting in Models in Machine Learning
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  6. Variance and Bias in Machine Learning
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  7. Assignment : Regularization and Optimization
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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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  2. Practical Demonstration -Keras
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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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  6. Loan Prediction Project Part 2
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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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