Scikit-Learn

Summary

Searching through massive datasets efficiently is a challenge, whether in image retrieval, recommendation systems, or semantic search. Faiss (Facebook AI Similarity Search) is a powerful open-source library developed by Meta to handle high-dimensional similarity search at scale.

It’s well-suited for tasks like:

• Image search: Finding visually similar images in a large database.
• Recommendation systems: Recommending items (products, movies, etc.) to users based on their preferences.
• Semantic search: Finding documents or text passages that are semantically similar to a given query.
• Clustering: Grouping similar vectors together.

In many of the upcoming projects in this blog I will be using it. It is a good local developer solution.

Summary

Imagine you have a dataset of customer profiles. How can you group similar customers together to tailor marketing campaigns? This is where K-Means clustering comes into play.

K-Means is a popular unsupervised learning algorithm used for clustering data points into distinct groups based on their similarities. It is widely used in various domains such as customer segmentation, image compression, and anomaly detection.

In this blog post, we’ll cover how K-Means works and demonstrate its implementation in Python using scikit-learn.