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Rapid AI Sentiment Analysis with Python and Scikit Ollama
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=_MZf85lseec
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. • If you found this video helpful, give us a like (👍) and subscribe to the Deep Charts channel for more tutorials on the latest AI, machine learning, and data science tools! • Full Code: https://github.com/deepcharts/project... • *Resources* • Ollama: https://ollama.com/ • Scikit-Ollama Library: https://andreaskarasenko.github.io/sk... • Scikit-LLM Library (The original package -- uses OpenAI ChatGPT models instead of Ollama): https://skllm.beastbyte.ai/ • FinVizFinance Library: https://finvizfinance.readthedocs.io/... • 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
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