Udemy course Master Cluster Analysis in Data Mining – Complete Course by Abhishek Kumar
Master Cluster Analysis in Data Mining – Complete Course is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the IT & Software category. Therefore, if you are looking to improve your IT & Software skills we recommend that you download Master Cluster Analysis in Data Mining – Complete Course udemy course.
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- Author: Abhishek Kumar
- Course rating: 3.5
- Category: IT & Software
- Modality: Online
- Status: Available
- Idiom: English
Abouth Abhishek Kumar
I am working as Computer Scientist at Adobe. I have 7 years of extensive experience in Programming. I am a Machine Learning enthusiast and have 4+ years of experience in Machine Learning.
What the udemy Master Cluster Analysis in Data Mining – Complete Course course teaches?
What you’ll learn Cluster Analysis algorithms for Data mining. Data Clustering Algorithms K-Means Clustering Hierarchical Clustering
Learn different Clustering Analysis algorithms from scratch with theory, examples and Python code
More information about the course Master Cluster Analysis in Data Mining – Complete Course
Welcome to Cluster Analysis in Data Mining! The problem of clustering is to take a collection of points and group them into ” clusters ” such that members of the same cluster are close, while members of different clusters are far. Clustering is an Unsupervised Learning subject and is very crucial for various applications like Categorization, Data preprocessing, Data Visualization, Outlier mining and many more. In this course we explore the different techniques used for clustering data. Cluster analysis itself is not one specific algorithm, but the general task that is to be solved. It can be achieved in different ways that differ significantly in their understanding of a Cluster and how to efficiently find those clusters. In general terms, clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can thus be formulated as a multi-objective Optimization problem. In this course we will study the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods, and density-based methods such as DBSCAN. We will also learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications. Course Contents: – – – – – – – – – – – – Clustering Algorithms K-Means Clustering Density-based clustering – DBSCAN Hierarchical Clustering Similarity Measures Clustering Validation Methods Good luck as you get started, and I hope you enjoy the course!