Course Summary
This course teaches the fundamentals of unsupervised learning and how it can be applied to real-world problems. You will learn various clustering and dimensionality reduction techniques and how to evaluate their effectiveness.Key Learning Points
- Learn clustering techniques such as K-Means and Hierarchical clustering
- Understand dimensionality reduction methods such as PCA and t-SNE
- Apply unsupervised learning to real-world problems
- Evaluate the effectiveness of clustering and dimensionality reduction techniques
Job Positions & Salaries of people who have taken this course might have
- USA: $120,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $120,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $140,000
- India: ₹1,200,000
- Spain: €55,000
- USA: $120,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $140,000
- India: ₹1,200,000
- Spain: €55,000
- USA: $70,000
- India: ₹600,000
- Spain: €30,000
Related Topics for further study
Learning Outcomes
- Apply clustering and dimensionality reduction techniques to real-world problems
- Evaluate the effectiveness of unsupervised learning methods
- Use unsupervised learning to gain insights from data
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and statistics
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- IBM Supervised Learning
- Machine Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
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.
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
Discover IBM's Unsupervised Learning course on Coursera. This course has received high praise from users for its clear, concise teaching style and practical applications.Key Aspect Users Liked About This Course
The practical applications of the course were highly rated by users.Pros from User Reviews
- Clear and concise teaching style
- Practical applications of the course
- Great for beginners
- Engaging content
- Helpful community
Cons from User Reviews
- Lack of advanced topics
- Some exercises were too easy
- Limited support from instructors
- Occasional technical issues
- Not enough hands-on practice
Mark J Grover
4.9 Raiting
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