Cluster Analysis and Unsupervised Machine Learning in Python

Cluster Analysis and Unsupervised Machine Learning in Python

What you’ll learn

  • Understand the regular K-Means algorithm
  • Understand and enumerate the disadvantages of K-Means Clustering
  • Understand the soft or fuzzy K-Means Clustering algorithm
  • Implement Soft K-Means Clustering in Code
  • Understand Hierarchical Clustering
  • Explain algorithmically how Hierarchical Agglomerative Clustering works
  • Apply Scipy’s Hierarchical Clustering library to data
  • Understand how to read a dendrogram
  • Understand the different distance metrics used in clustering
  • Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA
  • Understand the Gaussian mixture model and how to use it for density estimation
  • Write a GMM in Python code
  • Explain when GMM is equivalent to K-Means Clustering
  • Explain the expectation-maximization algorithm
  • Understand how GMM overcomes some disadvantages of K-Means
  • Understand the Singular Covariance problem and how to fix it

Who this course is for:

  • Students and professionals interested in machine learning and data science
  • People who want an introduction to unsupervised machine learning and cluster analysis
  • People who want to know how to write their own clustering code
  • Professionals interested in data mining big data sets to look for patterns automatically

Can I download Cluster Analysis and Unsupervised Machine Learning in Python course?

You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.
Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Cluster Analysis and Unsupervised Machine Learning in Python course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.
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