Rapid AI Sentiment Analysis with Python and Scikit Ollama

This tutorial will show you how to quickly set up an AI-powered sentiment analysis using Python and Scikit-Ollama right on your computer. We'll classify financial news headlines, first with a zero-shot classifier that predicts sentiment instantly without any training data, and then move to a few-shot classifier, where we train the model on a handful of labeled examples to refine and enhance its sentiment predictions.

You'll learn how to install and configure Meta's Llama 3 AI model using Ollama. You will also learn how to download and use alternative open source large language models like Google's Gemma 2 and Microsoft's Phi-3.

Scikit-Ollama is a wrapper built on Scikit-LLM (sklearn), which integrates large language models (LLMs) into the scikit-learn framework. This integration allows for seamless incorporation of LLM capabilities into existing machine learning workflows.

Whether you're a data scientist, machine learning engineer, or software developer, this guide will walk you through importing necessary libraries, fetching and preprocessing financial news data, and implementing zero-shot and few-shot classifiers.

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**Resources**

Chapters
0:00 Intro and Ollama Installation
0:35 Python Environment Setup
0:53 Fetching Stock News Headline Data
1:20 Zero Shot Classifier Setup and Prediction
1:57 Few Shot Classifier Training and Prediction