Clustering
Clustering
The clustering self-study is an implementation-oriented introduction to clustering.
- an exhaustive review of clustering
- an exhaustive description of and comparison between different algorithmic approaches to clustering
- a course on clustering with TensorFlow
- a tutorial on classification (as opposed to clustering)
Objectives:
- Define clustering for ML applications.
- Prepare data for clustering.
- Define similarity for your dataset.
- Compare manual and supervised similarity measures.
- Use the k-means algorithm to cluster data.
- Evaluate the quality of your clustering result.
Prerequisites
This course assumes you have:
- Completed Introduction to Machine Learning Problem Framing or have equivalent knowledge.
- Completed Machine Learning Crash Course or have equivalent knowledge.
- Completed Data Preparation and Feature Engineering or have equivalent knowledge.
- Basic knowledge of data distributions, such as Gaussian and power law distributions.
- Basic programming knowledge in Python.
Duration: 4 hours
Level: Advanced
Certification: Yes
Cost: Free
Language: English
Type: Self-Paced
Please note: these courses are provided by external sources, links are not actively managed or regularly updated, content might be moved or unavailable.