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Courses>Data Science>Cluster Analysis : Unsupervised Machine Learning in Python
DevelopmentCluster Analysis : Unsupervised Machine Learning in Python
Price:Paid
Length:1 hour
Content type:video
level:all levels
Updated:22 February 2024
Published:22 August 2022
Similar courses
Opportunities
Courses>Data Science>Cluster Analysis : Unsupervised Machine Learning in Python
Cluster Analysis : Unsupervised Machine Learning in Python
4.8 (1.0k)
1 hour
1006 students
What you will learn
1Describe the input and output of a clustering model
2Prepare data with feature engineering techniques
3Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models
4Determine the optimal number of clusters
5Use a variety of performance metrics such as Silhouette Score, Calinski-Harabasz Index and Davies-Bouldin Index.
Target audiences
1Beginners starting out to the field of Machine Learning.
2Industry professionals and aspiring data scientists.
3People who want to know how to write their clustering code.
Requirements
1Basic knowledge of Python Programming
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
Description

Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? Unsupervised machine learning is the underlying method behind a large part of this. Unsupervised machine learning algorithms analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without human intervention. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Clustering. This course provides the learners with the foundational knowledge to use Clustering models to create insights. You will become familiar with the most successful and widely used Clustering techniques, such as:

  • K-Means Clustering

  • Hierarchical Clustering

  • Mean Shift Clustering

  • DBSCAN : Density-Based Spatial Clustering of Applications with Noise

  • OPTICS : Ordering points to identify the clustering structure

  • Spectral Clustering

You will learn how to train clustering models to cluster and use performance metrics to compare different models. By the end of this course, you will be able to build machine learning models to make clusters using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!

Happy Learning.


Career Growth:

Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.

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Make the most out of your online education
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Careertail
Courses>Data Science>Cluster Analysis : Unsupervised Machine Learning in Python
DevelopmentCluster Analysis : Unsupervised Machine Learning in Python
Price:Paid
Length:1 hour
Content type:video
level:all levels
Updated:22 February 2024
Published:22 August 2022
Similar courses
Opportunities
Courses>Data Science>Cluster Analysis : Unsupervised Machine Learning in Python
Cluster Analysis : Unsupervised Machine Learning in Python
4.8 (1.0k)
1 hour
1006 students
What you will learn
1Describe the input and output of a clustering model
2Prepare data with feature engineering techniques
3Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models
4Determine the optimal number of clusters
5Use a variety of performance metrics such as Silhouette Score, Calinski-Harabasz Index and Davies-Bouldin Index.
Target audiences
1Beginners starting out to the field of Machine Learning.
2Industry professionals and aspiring data scientists.
3People who want to know how to write their clustering code.
Requirements
1Basic knowledge of Python Programming
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
Description

Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? Unsupervised machine learning is the underlying method behind a large part of this. Unsupervised machine learning algorithms analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without human intervention. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Clustering. This course provides the learners with the foundational knowledge to use Clustering models to create insights. You will become familiar with the most successful and widely used Clustering techniques, such as:

  • K-Means Clustering

  • Hierarchical Clustering

  • Mean Shift Clustering

  • DBSCAN : Density-Based Spatial Clustering of Applications with Noise

  • OPTICS : Ordering points to identify the clustering structure

  • Spectral Clustering

You will learn how to train clustering models to cluster and use performance metrics to compare different models. By the end of this course, you will be able to build machine learning models to make clusters using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!

Happy Learning.


Career Growth:

Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.

Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot