Introduction to Applied Unsupervised Learning

Summary

Learn from data using two popular unsupervised learning techniques.

Description

In this course, we will describe and demonstrate how to learn from data using unsupervised learning techniques during exploratory data analysis. The course is divided into two sections – one of which will focus on K-means clustering and the other will describe principal components analysis, commonly referred to as PCA. Each section includes demonstrations of important concepts, a quiz to solidify your understanding, and a lab to practice your skills.

Learning objectives

  • Identify relationships between data records using K-means clustering.

  • Identify patterns in a high-dimensional feature space using principal components analysis.

  • Learn from data using unsupervised learning techniques during exploratory data analysis.

Prerequisites

  • Intermediate experience with machine learning (experience using machine learning and data science libraries like scikit-learn and Pandas, knowledge of linear models)

  • Intermediate experience using the Databricks Workspace to perform data analysis (using Spark DataFrames, Databricks notebooks, etc.)

  • Beginning experience with machine learning concepts.

Learning path

  • This course is part of the data scientist learning path.

Proof of completion

  • Upon 80% completion of this course, you will receive a proof of completion.