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Discover how Named Tuples in Python enhance data organization by combining tuple efficiency with dictionary-like attribute access for clearer, more maintainable code structures.
Master coreference resolution in Python using spaCy-Experimental - implement advanced natural language processing techniques to track and link entity mentions across text.
Learn to implement transformer-based topic modeling using LeetTopic and create interactive Bokeh visualizations for text analysis in Python.
Learn to create interactive DataFrame applications using Streamlit and Pandas with just two lines of Python code, enabling quick data visualization and manipulation.
Learn how to efficiently save and serialize spaCy Doc containers using DocBin and Pickle methods for optimized data storage and retrieval in Python.
Master Streamlit's image grid functionality using columns to display collections of images efficiently in just 30 lines of Python code.
Learn to create interactive network analysis applications using Streamlit and st-link-analysis, featuring customizable visualizations, real-time interaction, and efficient data handling capabilities.
Discover how to generate synthetic data for machine learning and data analysis projects, with practical Python examples and implementation strategies.
Learn to implement audio and video transcription using Python and Whisper-MLX ASR, featuring step-by-step guidance on installation, setup, and execution of the transcription process.
Master the process of creating and publishing Python packages to PyPI through a step-by-step guide covering setup files, documentation, packaging with setuptools, and uploading with Twine.
Master the process of preparing and using Prodigy annotations to train SpanCat models in spaCy, focusing on data preparation, annotation techniques, and model training workflows.
Learn how to implement SpanCat in spaCy for real-world data annotation, focusing on project setup and effective data cultivation techniques for natural language processing.
Master the differences between spaCy's entity recognition tools and learn when to effectively implement NER, EntityRuler, SpanCat, and SpanRuler for optimal text processing results.
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