Course Details

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Unsupervised Learning


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Unsupervised learning can provide powerful insights on data without the need to annotate examples. In this course, you will learn several different techniques in machine learning.

Target Audience
Anyone interested in understanding machine learning and using it to solve problems

Prerequisites
None

Expected Duration (hours)
0.6

Lesson Objectives

Unsupervised Learning

  • start the course
  • describe unsupervised learning and some of the problems it can solve
  • describe rule association and how the apriori algorithm performs this task
  • use the apriori algorithm for rule association in Python
  • describe clustering and the types of problems it applies to
  • describe the k-means clustering algorithm
  • use SciKit Learn to build clusters in python
  • describe anomaly detection, the types of problems solved with anomaly detection, and some approaches to anomaly detection
  • use scikit learn to perform anomaly detection
  • describe the problems with dimensionality and why efforts to reduce dimensionality should be taken
  • describe principal component analysis for dimensionality reduction
  • use SciKit Learn to perform dimensionality reduction
  • perform dimensionality reduction and clustering tasks in Python
  • Course Number:
    sd_exml_a03_it_enus

    Expertise Level
    Beginner