Return to AI-DL-ML-LLM GitHub, AI-DL-ML-LLM Focused Companies, Hugging Face AI-DL-ML-LLM Services, AWS AI-DL-ML-LLM Services, Azure AI-DL-ML-LLM Services, GCP AI-DL-ML-LLM Services, IBM Cloud AI-DL-ML-LLM Services, Oracle Cloud AI-DL-ML-LLM Services, OpenAI AI-DL-ML-LLM Services, NVIDIA AI-DL-ML-LLM Services, Intel AI-DL-ML-LLM Services, Kubernetes AI-DL-ML-LLM Services, Apple AI-DL-ML-LLM Services, Meta-Facebook AI-DL-ML-LLM Services, Cisco AI-DL-ML-LLM Services
For the top 15 GitHub repos, ask for 10 paragraphs. e.g. Amazon SageMaker Features, Amazon SageMaker Alternatives, Amazon SageMaker Security, , Amazon SageMaker DevOps
In 2023, Twitter open-sourced its recommendation algorithm, providing insights into how content is curated and displayed to users. This move aimed to enhance transparency and allow developers to understand and contribute to the platform's content recommendation processes.
This project implements sentiment analysis on Twitter data using Natural Language Processing (NLP) and machine learning techniques. It classifies tweets as positive, negative, or neutral, leveraging the power of NLP to interpret human sentiments expressed on social media platforms.
https://github.com/priya-sammal/Twitter-Sentiment-Analysis-using-ML
This comprehensive project covers the entire process of sentiment analysis on Twitter data, from data collection and preprocessing to model building and deployment. It includes creating a dashboard and deploying the model as an online application, providing a practical guide to applying NLP and machine learning techniques to real-world data.
This project utilizes machine learning techniques to predict whether a Twitter account is a bot or a real user. By analyzing account behaviors and characteristics, it aims to identify automated accounts that may produce spam or malicious content on the platform.
https://github.com/jubins/MachineLearning-Detecting-Twitter-Bots
This project focuses on sentiment analysis of Twitter data using Support Vector Machine (SVM) models. It combines text preprocessing techniques, feature extraction methods, and machine learning algorithms to classify sentiment in tweets, assessing performance using metrics like ROC curve and AUC.
https://github.com/DivZyzz/Twitter-Sentiment-Analysis-using-SVM
This microservice-based web application analyzes Twitter sentiment in real-time. It demonstrates deploying a machine learning model, applying it in real-time, and scaling the model to handle live data streams, showcasing practical applications of NLP in social media analysis.
This repository contains a Python script for performing sentiment analysis on Twitter data using NLP techniques and machine learning algorithms. It preprocesses text data, extracts features using CountVectorizer, and trains a model to classify the sentiment of tweets.
https://github.com/sunnysavita10/Twitter-Sentiment-Analysis-using-NLP-and-Machine-Learning
Developed for a Harvard University course, this project involves detecting bots on Twitter using data science and machine learning techniques. It includes data acquisition, exploratory data analysis, and model development, providing a thorough approach to identifying automated accounts.
This project demonstrates sentiment analysis on Twitter data using Python and machine learning techniques. It involves data preprocessing, feature extraction, and model training to classify the sentiment expressed in tweets.
This project fine-tunes large NLP models using parameter-efficient methods like LoRA and QLoRA for sentiment analysis on the Kaggle Emotion Detection dataset. It achieves improvements in accuracy and F1 scores, highlighting the effectiveness of these methods in model adaptation.
https://github.com/V-man-45/Twitter-emotion-detection-using-Machine-learning
This project implements sentiment analysis on Twitter data using Natural Language Processing (NLP) and machine learning techniques. It classifies tweets as positive, negative, or neutral, leveraging the power of NLP to interpret human sentiments expressed on social media platforms.
https://github.com/priya-sammal/Twitter-Sentiment-Analysis-using-ML
This comprehensive project covers the entire process of sentiment analysis on Twitter data, from data collection and preprocessing to model building and deployment. It includes creating a dashboard and deploying the model as an online application, providing a practical guide to applying NLP and machine learning techniques to real-world data.
This project utilizes machine learning techniques to predict whether a Twitter account is a bot or a real user. By analyzing account behaviors and characteristics, it aims to identify automated accounts that may produce spam or malicious content on the platform.
https://github.com/jubins/MachineLearning-Detecting-Twitter-Bots
This project focuses on sentiment analysis of Twitter data using Support Vector Machine (SVM) models. It combines text preprocessing techniques, feature extraction methods, and machine learning algorithms to classify sentiment in tweets, assessing performance using metrics like ROC curve and AUC.
https://github.com/DivZyzz/Twitter-Sentiment-Analysis-using-SVM
This microservice-based web application analyzes Twitter sentiment in real-time. It demonstrates deploying a machine learning model, applying it in real-time, and scaling the model to handle live data streams, showcasing practical applications of NLP in social media analysis.
This repository contains a Python script for performing sentiment analysis on Twitter data using NLP techniques and machine learning algorithms. It preprocesses text data, extracts features using CountVectorizer, and trains a model to classify the sentiment of tweets.
https://github.com/sunnysavita10/Twitter-Sentiment-Analysis-using-NLP-and-Machine-Learning
Developed for a Harvard University course, this project involves detecting bots on Twitter using data science and machine learning techniques. It includes data acquisition, exploratory data analysis, and model development, providing a thorough approach to identifying automated accounts.
This project demonstrates sentiment analysis on Twitter data using Python and machine learning techniques. It involves data preprocessing, feature extraction, and model training to classify the sentiment expressed in tweets.
This project fine-tunes large NLP models using parameter-efficient methods like LoRA and QLoRA for sentiment analysis on the Kaggle Emotion Detection dataset. It achieves improvements in accuracy and F1 scores, highlighting the effectiveness of these methods in model adaptation.
https://github.com/V-man-45/Twitter-emotion-detection-using-Machine-learning
This project focuses on detecting bots on Twitter using supervised machine learning algorithms. It analyzes user behavior and account features to classify accounts as bots or humans, contributing to efforts in maintaining the integrity of social media platforms.