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.
Learning Objectives
Introducing Unsupervised Learning
- start the course
- describe unsupervised learning and some of the problems it can solve
Rule Association
- describe rule association and how the apriori algorithm performs this task
- use the apriori algorithm for rule association in Python
Cluster Analysis
- describe clustering and the types of problems it applies to
- describe the k-means clustering algorithm
- use SciKit Learn to build clusters in python
Anomaly Detection
- describe anomaly detection, the types of problems solved with anomaly detection, and some approaches to anomaly detection
- use scikit learn to perform anomaly detection
Dimensionality Reduction
- 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
Practice: Unsupervised Learning
- perform dimensionality reduction and clustering tasks in Python