Categories: AI/ML Research

LLM Embeddings vs TF-IDF vs Bag-of-Words: Which Works Better in Scikit-learn?

Machine learning models built with frameworks like scikit-learn can accommodate unstructured data like text, as long as this raw text is converted into a numerical representation that is understandable by algorithms, models, and machines in a broader sense.

Feature Engineering with LLM Embeddings: Enhancing Scikit-learn Models

Large language model embeddings, or LLM embeddings, are a powerful approach to capturing semantically rich information in text and utilizing it to leverage other machine learning models — like those trained using Scikit-learn — in tasks that require deep contextual understanding of text, such as intent recognition or sentiment analysis.

July 18, 2025

In "AI/ML Research"

Zero-Shot and Few-Shot Classification with Scikit-LLM

In this article, you will learn: • how Scikit-LLM integrates large language models like OpenAI's GPT with the Scikit-learn framework for text analysis.

July 23, 2025

In "AI/ML Research"

Use PyTorch Deep Learning Models with scikit-learn

The most popular deep learning libraries in Python for research and development are TensorFlow/Keras and PyTorch, due to their simplicity. The scikit-learn library, however, is the most popular library for general machine learning in Python. In this post, you will discover how to use deep learning models from PyTorch with…

February 9, 2023

In "AI/ML Research"

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