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