Unsupervised Learning is a type of 🤖 ML that discovers patterns in unlabeled data by grouping similar inputs together. It’s like learning without being told the right answers.
Notes
Info
It’s like exploring without a map.
Summary
Unsupervised Learning is analogous to a curious child exploring their surroundings, discovering patterns and structures on their own
- For instance, in customer segmentation, data about customers’ purchasing behavior are used to group similar customers together
- The model learns to identify patterns among the customers, allowing for targeted marketing strategies
graph TD; A[Input Data]:::main --> B[Model]:::main; B --> C[Clusters/Groupings]:::main; C -.->|Goal is to find meaningful patterns in|A; classDef main stroke:#eee
The essence of unsupervised learning is allowing machines to learn from unlabeled data and discover hidden patterns or groupings.
TakeAways
- 📌 Unsupervised Learning uses unlabeled datasets to train models capable of finding patterns among inputs.
- Examples include Clustering and rule mining tasks
- Models learn from input similarities without being given explicit output labels
- 💡 Algorithms such as K-means clustering, hierarchical clustering, and Density-based spatial clustering of applications with noise are common in unsupervised learning
- 🔍 ⚡ Model performance checks should be done cyclically by using metrics
Process
- 🗄️ Data Collect: Collect a dataset (unlabeled)
- 📁 Preprocess the Data: Clean and normalize the data
- 🧠 Choose an unsupervised learning algorithm: Select a suitable algorithm for finding patterns, e.g., K-means clustering
- 🏋️ Train the model: Train the chosen algorithm on the dataset to find meaningful groupings or patterns
- 🦉 Monitor and tune the model: Keep monitoring and tuning the model to ensure it fits the data.
Thoughts
- ⛰️ Great for exploration : Best suited for tasks where no prior labels exist, allowing machines to learn from data’s inherent structure.
- 💭 No explicit guidance : Models must discover patterns autonomously without being given output labels.
- 🕵️ Potential for hidden insights : Unsupervised learning can reveal unexpected structures and groupings within the data.