Complete Machine Learning Course > Unsupervised Learning Algorithms

K-Means Clustering in Machine Learning

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Unsupervised learning algorithm K-Means clustering Unlike supervised learning, there is no labelled data for this grouping. K-Means divides things into clusters based on their similarities and differences with objects in other groups.

They are frequently confused with one another. The 'K' in the K-Means Clustering algorithm is not the same as the 'K' in the KNN algorithm. KNN is a supervised learning method for classification, whereas k-Means Clustering is an unsupervised learning approach for clustering.

The number of clusters into which you want to divide your data points, i.e. the value of K, must be pre-determined in k-means clustering, whereas data is automatically organised into a tree shape form in hierarchical clustering (dendrogram).

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Shafi Akhtar
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Aniket Kumar prasad
5

Very helpful and easy to understand all the concepts, best teacher for learning ML.

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Rishu Shrivastav
5

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
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

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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
    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
    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
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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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  13. Plotting Graphs in Machine Learning - Part 1
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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
    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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  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

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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
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  5. Startup Prediction using Multiple Regression in Machine Learning
    25m 13s
  6. Support Vector Regressor in Machine Learning
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    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
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    7m 15s
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  4. FP Growth in Machine Learning
    7m 43s
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    11m 46s
  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
    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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  7. Assignment : Regularization and Optimization
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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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  1. Course Summary
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  1. Interview Questions Part 1
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    12m 23s
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  1. Career Guideline
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