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Unsupervised Machine Learning

Approx. 9 hours to complete

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Course Summary

This course teaches unsupervised machine learning techniques for clustering and dimensionality reduction, and their applications in real-world scenarios.

Key Learning Points

Learn unsupervised machine learning techniques for clustering and dimensionality reduction
Apply unsupervised machine learning techniques in real-world scenarios
Understand the limitations and pitfalls of unsupervised machine learning

Learning Outcomes

Ability to apply unsupervised machine learning techniques to real-world scenarios
Understanding of the limitations and pitfalls of unsupervised machine learning
Skills in clustering and dimensionality reduction techniques

Prerequisites or good to have knowledge before taking this course

Basic knowledge of machine learning algorithms
Familiarity with Python programming language

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on exercises

Similar Courses

IBM Supervised Machine Learning: Regression
Unsupervised Learning with Python

Related Education Paths

Notable People in This Field

Andrew Ng
Fei-Fei Li

Related Books

Description

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.

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 Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. What skills should you have? 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.

Outline

Introduction to Unsupervised Learning and K Means
Course Introduction
Introduction to Unsupervised Learning - Part 1
Introduction to Unsupervised Learning - Part 2
Introduction to Clustering
K-Means - Part 1
K-Means - Part 2
K-Means - Part 3
K-Means - Part 4
K Means Notebook - Part 1
K Means Notebook - Part 2
K Means Notebook - Part 3
K Means Demo (Activity)
Summary
Introduction to Unsupervised Learning
K Means Clustering
End of Module

Selecting a clustering algorithm
Distance Metrics - Part 1
Distance Metrics - Part 2
Curse of Dimensionality Notebook - Part 1
Curse of Dimensionality Notebook - Part 2
Curse of Dimensionality Notebook - Part 3
Curse of Dimensionality Notebook - Part 4
Hierarchical Agglomerative Clustering - Part 1
Hierarchical Agglomerative Clustering - Part 2
DBSCAN - Part 1
DBSCAN - Part 2
Mean Shift
Comparing Algorithms
Clustering Notebook - Part 1
Clustering Notebook - Part 2
Clustering Notebook - Part 3
Clustering Notebook - Part 4
Curse of Dimensionality Demo (Activity)
Clustering Demo (Activity)
Summary
Distance Metrics
Clustering Algorithms
Comparing Clustering Algorithms
End of Module

Dimensionality Reduction
Dimensionality Reduction - Part 1
Dimensionality Reduction - Part 2
PCA Notebook - Part 1
PCA Notebook - Part 2
PCA Notebook - Part 3
Non Negative Matrix Factorization
Non Negative Matrix Factorization Notebook - Part 1
Non Negative Matrix Factorization Notebook - Part 2
Dimensionality Reduction Imaging Example
Principal Component Analysis (Activity)
Non Negative Matrix Factorization (Activity)
Summary
Dimensionality Reduction
Non Negative Matrix Factorization
End of Module

Summary of User Reviews

Learn about IBM Unsupervised Machine Learning on Coursera. Discover what users think about this course, including their overall rating and key aspects they found good. Read on for the most common pros and cons mentioned by users.

Key Aspect Users Liked About This Course

The course content is comprehensive and well-structured, making it easy to follow along and understand.

Pros from User Reviews

Comprehensive and well-structured content
Great explanations and examples
Engaging and interactive assignments and quizzes
Excellent support from the instructor and community
Good balance of theory and practical applications

Cons from User Reviews

Some technical issues with the platform
Could benefit from more real-world examples
May be too basic for some users
Lack of detailed explanations for certain topics
Can be time-consuming and require a lot of effort

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