Unsupervised Learning Techniques

Unsupervised Learning Techniques (1 day)

In this course we will tackle the most important methodologies, algorithms and ideas of unsupervised learning techniques. You will learn the power of unsupervised learning to uncover underlying patterns or concepts contained in large datasets, automatically group objects into meaningful clusters, and refine your features to enrich unsupervised learning endeavors. This course covers the most important algorithms of unsupervised learning – from Cluster Analysis, Dimensionality Reduction to Association Analysis and Collaborative Filtering Techniques. The course will cover modern thinking on model evaluation, model selection, and novel ideas of model deployment. Exercises are offered for KNIME, R, Python, SAS and Spark.

Course Outline

  • Introduction to Unsupervised Learning and challenges
  • Introduction to Feature Engineering
  • Data Cleansing (Missing Value Treatment, Outlier Treatment)
  • Sampling Methodology
  • Cluster Analysis
  • Principal Component Analysis
  • Applications for Dimensionality Reduction
  • Association Analysis
  • Collaborative Filtering
  • Model Assessment
  • Model Deployment

Who should attend

Data Scientists, statisticians, business analysts, market researchers, and information technology professionals who need to get started with unsupervised learning and want to make better use of their data.

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