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Course Description
Unsupervised machine learning allows a practitioner to surface emergent patterns in data. These emergent patterns can give a machine learning practitioner important information about the structure of the data that can be used in many different applications.
In these lessons, we will discuss two important applications of unsupervised machine learning that showcase the two overarching styles of unsupervised machine learning: clustering, which groups data cases together, and dimension reduction, which groups data variables together. Clustering can be used in a customer segmentation which attempts to find groups of customers with similar behaviours, attitudes, and psychographic. Products can then be developed to specifically target those groups. Dimension reduction can be used to define the optimal genre for a new radio station by looking at similarities between musical styles that could be grouped together. These lessons will also discuss how unsupervised machine learning can be used in feature engineering. You’ll also explore semi-supervised machine learning and its applications.
What You'll Learn
- Differentiate between problems that can be solved by clustering and dimension reduction
- Determine data-collection methods that yield appropriate data for clustering and dimension reduction
- Apply best practices in design decisions to minimize issues with unsupervised machine learning models
- Use clustering to group data cases together, such as in customer segmentation
- Use dimension reduction to group data variables together based on similarities
- Explore how unsupervised machine learning supports feature engineering and semi-supervised learning
Key Takeaways
- Unsupervised machine learning surfaces emergent patterns that reveal the structure of data for use in many applications.
- Clustering and dimension reduction are the two overarching styles of unsupervised machine learning, grouping data cases and data variables respectively.
- Clustering can power customer segmentation by finding groups of customers with similar behaviours, attitudes, and psychographics so products can target those groups.
- Dimension reduction can identify an optimal radio-station genre by grouping similar musical styles together.
- Unsupervised machine learning can be applied to feature engineering, and semi-supervised machine learning has its own applications.
Frequently Asked Questions
What does this course cover?
It covers two important applications of unsupervised machine learning representing its two overarching styles: clustering, which groups data cases together, and dimension reduction, which groups data variables together. It also covers using unsupervised learning for feature engineering and explores semi-supervised machine learning and its applications.
What are some real-world applications discussed in the course?
The course discusses customer segmentation through clustering to find groups of customers with similar behaviours, attitudes, and psychographics, and using dimension reduction to define the optimal genre for a new radio station by grouping similar musical styles.
What skills will I gain from this course?
The course is associated with skills in feature learning, machine learning, machine learning algorithms, machine learning methods, supervised learning, and unsupervised learning.
What lessons are included?
The course includes lessons on Unsupervised Machine Learning, Clustering, Dimension Reduction, Unsupervised Learning for Feature Engineering, and Semi-Supervised Machine Learning.
What will I be able to do after completing the course?
You will be able to differentiate between problems solvable by clustering versus dimension reduction, determine appropriate data-collection methods for these methods, and apply best practices in design decisions to minimize issues with the unsupervised machine learning model.
