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15 articles
Learn to build an AI-powered sentiment analysis tool that can process text and determine sentiment, similar to what companies like Samsung might use to understand labor tensions during AI implementation.
Learn to build a legal document classification system using Python and machine learning, similar to what companies like Lexroom use in their civil-law legal AI platforms.
Learn to build an AI-powered legal case analysis system that processes legal documents, analyzes statute of limitations, and matches cases to similar precedents.
Learn how to build an AI authorship detection system that can distinguish between human-authored and AI-generated content in scripts, using natural language processing techniques.
Learn to build a sentiment analysis tool using Python and Hugging Face Transformers library. This tutorial teaches you how to analyze text emotions and understand AI sentiment detection technology.
Learn to build a complete PII detection and redaction pipeline using OpenAI Privacy Filter and transformer models. This intermediate tutorial teaches you how to identify and automatically redact sensitive data from text.
Learn to build a document analysis tool that can examine legal documents for key terms, sentiment, and entities - perfect for researching complex legal cases like the OpenAI trial.
Learn to build an AI-powered coding assistant that generates and executes code from natural language prompts, similar to tools like Cursor that SpaceX is investing in.
Learn to build a basic financial chatbot similar to Revolut's new AI assistant AIR, understanding how AI financial assistants work through hands-on coding.
Learn how to work with large language models using Python and Hugging Face Transformers, demonstrating core AI techniques similar to those used by OpenAI.
Learn how to use Microsoft's new Harrier-OSS-v1 multilingual embedding models to generate semantic embeddings and calculate similarity scores across multiple languages.
Learn how to implement and compare BM25 and Retrieval-Augmented Generation (RAG) for information retrieval, understanding their fundamental differences in document ranking and response generation.