Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.
-
Updated
May 29, 2024 - Jupyter Notebook
Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.
Welcome to the Machine Learning Repository! This repository is a collection of notebooks showcasing various machine learning projects and implementations. It incluedes Decision tree algorithm, Random forest , Support vector machine etc.
A dark web analysis tool.
AlmaBetter Capstone Project -Classification model to predict the sentiment of COVID-19 tweets. The tweets have been pulled from Twitter and manual tagging has been done then.
Machine Learning model capable of accurately predicting the Rate of Interest (ROI) from bureau data
Web platform allows users to upload CSV files and train a machine learning model using the uploaded data
A Deep Learning application designed to detect plant 🌱diseases using images of plant leaves🌿, powered by TensorFlow technology.
This project fine-tunes the LLaMA-3.1-8B model using LoRA adapters for parameter-efficient training. It leverages chat templates for conversation structuring, utilizes 4-bit quantization for memory efficiency, and saves the fine-tuned model for deployment on the Hugging Face Hub.
The Titanic classification problem involves predicting whether a passenger on the Titanic survived or not, based on various features available about each passenger. The sinking of the Titanic in 1912 is one of the most infamous maritime disasters in history, and this dataset has been widely used as a benchmark for predictive modeling.
This is a Data Science task related to kaggle challenge of Titanic Spaceship
This repository serves as a comprehensive resource for understanding and implementing various feature selection techniques, gaining familiarity with Jupyter Notebook, and mastering the process of model training and evaluation
Data Analysis, Model Training, Model Deployment.
One notebook trains a vegetable classification model with InceptionV3 using TensorFlow and Keras. The second notebook showcases the pre-trained model's inference on vegetable categories, loading InceptionV3 and enhancing image features. Together, they offer a compact solution for vegetable classification through deep learning.
Predicting heart failure using Decision Tree algorithm with a dataset sourced from Kaggle. Achieved 99% accuracy, demonstrating robust performance as a binary classifier.
This project detects if the card holder will default on the credit payment on the following month or not by implementation of various ML Classification Algorithms in a modular coding format
This is a project which allows a user to translate, summarize, paraphrase and humanize it. It is basically a web application that allows user to enhance their content by providing these features.
This project focuses on predicting the prices of clothes based on various features such as category, size, and color. Leveraging the power of machine learning, specifically supervised learning algorithms, we aim to build a robust predictive model capable of estimating prices with high accuracy.
This project utilizes a machine learning model where consumer brand data is employed. Initially, a preliminary model is developed, followed by a refined model using a process called 'fine-tuning' to improve results. Additionally, a comprehensive testing suite has been created to validate accuracy and reliability of the model's predictions.
This project aims to predict the prices of cars based on various features such as year of manufacture, brand, mileage, and other relevant factors. Leveraging machine learning algorithms, this project explores different regression techniques to create an accurate model for car price prediction.
Add a description, image, and links to the modeltraining topic page so that developers can more easily learn about it.
To associate your repository with the modeltraining topic, visit your repo's landing page and select "manage topics."