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Course Description
In this KnowledgeCity course on Python for Data Science Advanced: Unsupervised Learning, you’ll learn about unsupervised machine learning by studying principals, component analysis, and clustering techniques. We’ll go over principal component analysis, and how to cluster data using K-means in Python. You’ll also learn about the DBSCAN unsupervised machine learning algorithm, and we’ll spend time covering affinity propagation in the realm of machine learning.
Unsupervised learning is a way for machines to find patterns or groups in data without being told what those patterns are. We’ll explore DBSCAN, an acronym that stands for Density-Based Spatial Clustering of Applications with Noise. It's an unsupervised machine learning algorithm used for clustering data points in a dataset. K-means is a popular unsupervised machine learning algorithm used for clustering and data partitioning. Its primary goal is to group data points into clusters. We’ll also explore affinity propagation, a clustering algorithm used in unsupervised machine learning that doesn't require you to specify the number of clusters beforehand—instead, it discovers the number of clusters from the data all on its own.
What You'll Learn
- Explain how unsupervised machine learning works and finds patterns or groups in data without being told what those patterns are
- Apply principal component analysis to data
- Cluster data using the K-means algorithm in Python
- Use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to cluster data points
- Find clusters within data using affinity propagation without specifying the number of clusters beforehand
- Describe the functions of various unsupervised learning algorithms
Key Takeaways
- Unsupervised learning lets machines find patterns or groups in data without being told what those patterns are.
- K-means is a popular unsupervised machine learning algorithm used for clustering and data partitioning, grouping data points into clusters.
- DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise and is an unsupervised algorithm used for clustering data points in a dataset.
- Affinity propagation does not require you to specify the number of clusters beforehand; it discovers the number of clusters from the data on its own.
- Principal component analysis is one of the techniques covered for working with data in unsupervised learning.
Frequently Asked Questions
What clustering techniques does this course cover?
The course covers principal component analysis and clustering techniques including K-means, DBSCAN, and affinity propagation.
What is DBSCAN as taught in this course?
DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise, an unsupervised machine learning algorithm used for clustering data points in a dataset.
Do I need to specify the number of clusters when using affinity propagation?
No. Affinity propagation does not require you to specify the number of clusters beforehand; it discovers the number of clusters from the data on its own.
What skills will I gain from this course?
You will gain skills in DBSCAN, deep linking, K-means clustering, and unsupervised learning.
What programming language is used in this course?
The course uses Python, for example to cluster data using K-means.
