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Unsupervised Machine Learning: Cluster Analysis Algorithms


  Unsupervised Machine Learning: Cluster Analysis Algorithms

Unsupervised Machine Learning: Cluster Analysis Algorithms | Udemy Coupon ED

Cluster Analysis: core concepts, working, evaluation of KMeans, Meanshift, DBSCAN, OPTICS, Hierarchical clustering Get Udemy Course
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What you'll learn
  • Understand the KMeans Algorithm and implement it from scratch
  • Learn about various cluster evaluation metrics and techniques
  • Learn how to evaluate KMeans algorithm and choose its parameter
  • Learn about the limitations of original KMeans algorithm and learn variations of KMeans that solve these limitations
  • Understand the DBSCAN algorithm and implement it from scratch
  • Learn about evaluation, tuning of parameters and application of DBSCAN
  • Learn about the OPTICS algorithm and implement it from scratch
  • Learn about the cluster ordering and cluster extraction in OPTICS algorithm
  • Learn about evaluation, parameter tuning and application of OPTICS algorithm
  • Learn about the Meanshift algorithm and implement it from scratch
  • Learn about evaluation, parameter tuning and application of Meanshift algorithm
  • Learn about Hierarchical Agglomerative clustering
  • Learn about the single linkage, complete linkage, average linkage and Ward linkage in Hierarchical Clustering
  • Learn about the performance and limitations of each Linkage Criteria
  • Learn about applying all the clustering algorithms on flat and non-flat datasets
 Description
 
Clustering is the activity of splitting the data into partitions that give an insight about the unlabelled data. It gives a structure to the data by grouping similar data points

We see these clustering algorithms almost everywhere in our everyday life. Cluster Analysis has and always will be a staple for all Machine Learning. Clustering has its applications in many Machine Learning tasks: label generation, label validation, dimensionality reduction, semi supervised learning, Reinforcement learning, computer vision, natural language processing.
For a data scientist, cluster analysis is one of the first tools in their arsenal during exploratory analysis, that they use to identify natural partitions in the data.
In this course, you will learn some of the most important algorithms used for Cluster Analysis
Each dataset and feature space is unique. You cannot use a one-size-fits-all method for recognizing patterns in the data. Each algorithm has its own purpose.
By studying the core concepts and working in detail and writing the code for each algorithm from scratch, will empower you, to identify the correct algorithm to use for each scenario.
Some algorithms are fast and are a good starting point to quickly identify the pattern of the data
And some algorithms are slow but more precise, and allow you to capture the pattern very accurately.
You will get to understand each algorithm in detail, which will give you the intuition for tuning their parameters and maximizing their utility

In this course, for cluster analysis you will learn five clustering algorithms:
You will learn about KMeans and Meanshift. These are two centroid based algorithms, which means their definition of a cluster is based around the center of the cluster.
Next you will study DBSCAN and OPTICS. These are density based algorithms, in which they find high density zones in the data and for such continuous density zones, they identify them as clusters.
Another type of algorithm that you will learn is Agglomerative Clustering, a hierarchical style of clustering algorithm, which gives us a hierarchy of clusters.

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