Data Science – Data Mining Unsupervised Learning R & Python

Learners will understand about Data Science– Data Mining Unsupervised Learning in developing & analyzing Data Science projects or Artificial Intelligence projects. Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data.This course includes practical approach and discussed about Clustering segmentation, Dimension reduction, Association rules, Recommended system, Network Analytics, Text mining etc,.

Clustering segmentation : In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. Hierarchical clustering, K means clustering are most commonly used clustering algorithms. Understand the different mathematical approaches to perform data segregation. Also learn about variations in K–means clustering like K–medoids, K–mode techniques, learn to handle large data sets using CLARA technique.

Dimension Reduction (PCA) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

Association rules : Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence and Lift used to define the rules with the help of Apriori algorithm. Learn pros and cons of each of the metrics used in Association rules