Home » LESSON 5 – K-Means Clustering | k-means คือ

LESSON 5 – K-Means Clustering | k-means คือ

LESSON 5 – K-Means Clustering

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LESSON 5 - K-Means Clustering

Machine Learning Tutorial Python – 13: K Means Clustering Algorithm

K Means clustering algorithm is unsupervised machine learning technique used to cluster data points. In this tutorial we will go over some theory behind how k means works and then solve income group clustering problem using sklearn, kmeans and python. Elbow method is a technique used to determine optimal number of k, we will review that method as well.
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Code: https://github.com/codebasics/py/blob/master/ML/13_kmeans/13_kmeans_tutorial.ipynb
data link: https://github.com/codebasics/py/tree/master/ML/13_kmeans
Exercise solution: https://github.com/codebasics/py/blob/master/ML/13_kmeans/Exercise/13_kmeans_exercise.ipynb
Topics that are covered in this Video:
0:00 introduction
0:08 Theory Explanation of Supervised vs Unsupervised learning and how kmeans clustering works. kmeans is unsupervised learning
5:00 Elbow method
7:33 Coding (start) (Cluster people income based on age)
9:38 sklearn.cluster KMeans model creation and training
14:56 Use MinMaxScaler from sklearn
24:07 Exercise (Cluster iris flowers using their petal width and length)
Next Video:
Machine Learning Tutorial Python 14: Naive Bayes Part 1: https://www.youtube.com/watch?v=PPeaRcr1OI\u0026list=PLeo1K3hjS3uvCeTYTeyfe0rN5r8zn9rw\u0026index=15
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Jupyter Notebook: https://www.youtube.com/watch?v=q_BzsPxwLOE\u0026list=PLeo1K3hjS3uuZPwzACannnFSn9qHn8to8
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Scikit learn tutorials
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Machine Learning Tutorial Python - 13:  K Means Clustering Algorithm

Clustering 2: soft vs. hard clustering

Full lecture: http://bit.ly/Kmeans
A hard clustering means we have nonoverlapping clusters, where each instance belongs to one and only one cluster. In a soft clustering method, a single individual can belong to multiple clusters, often with a confidence (belief) associated with each cluster.

Clustering 2: soft vs. hard clustering

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Clustering 1: monothetic vs. polythetic

Full lecture: http://bit.ly/Kmeans
The aim of clustering is to partition a population into subgroups (clusters). Clusters can be monothetic (where all cluster members share some common property) or polythetic (where all cluster members are similar to each other in some sense).

Clustering 1: monothetic vs. polythetic

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