Unsupervised Learning – W7104GSPVC
Enrol Details
Course Code: W7104G Brand: IBM Analytics Category: Analytics Skill Level: Intermediate Duration: 8H Modality:SPVC Audience
This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.
Prerequisites
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.
Short Summary
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning Overview
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:
- IBM Learning for Data and AI Individual Subscription (SUBR022G)
- IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
- IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)
Topic
1. Introduction to Unsupervised Learning and K Means
2. Selecting a clustering algorithm
3. Dimensionality Reduction
Objectives
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.