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7 Views· 09 October 2022

K Means Clustering Algorithm | K Means In Python | Machine Learning Algorithms |Simplilearn

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This K Means clustering algorithm tutorial video will take you through machine learning basics, types of clustering algorithms, what is K Means clustering, how does K Means clustering work with examples along with a demo in python on K-Means clustering - color compression. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K Means clustering work. This video is a part of the Machine Learning with Python Series.

Below topics are covered in this K-Means Clustering Algorithm Tutorial:
1. Types of Machine Learning? ( 07:08 )
2. What is K Means Clustering? ( 00:10 )
3. Applications of K Means Clustering ( 09:27 )
4. Common distance measure ( 10:20 )
5. How does K Means Clustering work? ( 12:27 )
6. K Means Clustering Algorithm ( 20:08 )
7. Demo In Python: K Means Clustering ( 26:20 )
8. Use case: Color compression In Python ( 38:38 )

Dataset Link - https://drive.google.com/drive..../folders/1RdPdB3Fkqo

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About Simplilearn Machine Learning course:

A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

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Why learn Machine Learning?

Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

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What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

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