Unsupervised Machine Learning

This course is part of multiple programs. Learn more

Instructors: Mark J Grover +3 more

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Skills you'll gain

  •   Dimensionality Reduction
  •   Feature Engineering
  •   Scikit Learn (Machine Learning Library)
  •   Algorithms
  •   Machine Learning
  •   Text Mining
  •   Data Science
  •   Machine Learning Algorithms
  •   NumPy
  •   Data Mining
  •   Natural Language Processing
  •   Unsupervised Learning
  •   Statistical Machine Learning
  •   Data Analysis
  •   Linear Algebra
  •   Big Data
  • There are 7 modules in this course

    By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

    Distance Metrics & Computational Hurdles

    Selecting a Clustering Algorithm

    Dimensionality Reduction

    Nonlinear and Distance-Based Dimensionality Reduction

    Matrix Factorization

    Final Project

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