Size: 881.47 MB
Data science techniques for pattern recognition, data mining, kmeans clustering, and hierarchical clustering, and KDE.
What you’ll learn
 Understand the regular KMeans algorithm

Understand and enumerate the disadvantages of KMeans Clustering

Understand the soft or fuzzy KMeans Clustering algorithm
 Implement Soft KMeans 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 KMeans Clustering
 Explain the expectationmaximization algorithm
 Understand how GMM overcomes some disadvantages of KMeans
 Understand the Singular Covariance problem and how to fix it
Requirements
 Know how to code in Python and Numpy
 Install Numpy and Scipy
Description
Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. calculus
 linear algebra
 probability
 Python coding: if/else, loops, lists, dicts, sets
 Numpy coding: matrix and vector operations, loading a CSV file
 Watch it at 2x.
 Take handwritten notes. This will drastically increase your ability to retain the information.
 Write down the equations. If you don’t, I guarantee it will just look like gibberish.
 Ask lots of questions on the discussion board. The more the better!
 Realize that most exercises will take you days or weeks to complete.
 Write code yourself, don’t just sit there and look at my code.
 Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
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
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