IBM - Unsupervised Machine Learning
- Offered byCoursera
Unsupervised Machine Learning at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Unsupervised Machine Learning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 9 hours to complete
- English Subtitles: English
Unsupervised Machine Learning at Coursera Course details
- 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.
- This course is part of multiple programs
- This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- IBM Introduction to Machine Learning Specialization
- IBM Machine Learning Professional Certificate
Unsupervised Machine Learning at Coursera Curriculum
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
Unsupervised Machine Learning at Coursera Admission Process
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