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Learn to create generative art using Python and Samila library in a Streamlit web app. Explore mathematical functions for unique digital artwork generation.
Enhance Streamlit apps with custom button CSS, web scraping, and interactive features. Deploy on Hugging Face Spaces and Streamlit Cloud for a polished Wordle spoiler application.
Build a Python web app to extract tables from PDFs using Camelot and Streamlit. Learn file uploading, data conversion, and table display techniques.
Learn to build and deploy a Streamlit app that assists with the popular game Wordle, using Python and NLTK. Perfect for non-native English speakers seeking word clues.
Learn to deploy static websites on Hugging Face Spaces for free, including local deployment and setting up CI/CD pipelines from GitHub for automated deployment.
Learn to build custom Named Entity Recognition models using spaCy v3, including data annotation with free tools and training techniques for improved NLP applications.
Learn topic modeling with BERTopic, extract insights from Kaggle notebooks, and create interactive visualizations. Master applied NLP techniques using transformers and c-TF-IDF for interpretable, dense topic clusters.
Learn to use Amazon SageMaker Studio Lab, a free tool for machine learning and data science with Jupyter notebooks, Python, R, and GPU support. Explore its features and advantages over alternatives.
Learn to convert English numbers to numeric values using Numerizer, build a Streamlit app, and deploy it on Hugging Face Spaces. Gain practical NLP skills through hands-on Python coding and web app development.
Explore AutoXGB, a Python library for automated machine learning using XGBoost, Optuna, and FastAPI. Learn its parameters and application in a Kaggle competition.
Learn to deploy Streamlit ML web apps on Hugging Face Spaces using GitHub Actions. Covers workflow setup, code editing with GitHub.dev, and continuous deployment for efficient ML app hosting.
Explore SHAP and Shapley values for interpretable machine learning, focusing on summary plots and dependence plots to enhance model explainability and insights.
Create engaging data comic strips using Gramener's ComicGen and Google Sheets, enhancing your storytelling skills with visual narratives.
Explore SHAP and Shapley Values for interpretable machine learning. Learn local interpretation techniques, Python implementation, and visualization methods for explainable AI.
Explore Partial Dependence Plots for interpretable machine learning. Learn to visualize, build, and interpret PDPs, including 2D plots, to enhance model explainability and insights.
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