AI PROJECTS SOURCE CODE

Create a voice cloning application using deep learning, with steps for data preparation, model training, and voice inference to generate realistic and customizable voice clones.

Automatic Eye Cataract Detection is an AI-based tool leveraging YOLOv8 for precise and quick cataract diagnosis, enhancing efficiency and accuracy in eye care.

This project utilizes YOLOv8 to build a crop disease detection and classification system in Google Colab. The system processes images and videos to identify diseases, providing an interactive interface for real-time analysis using Gradio.

This tutorial on Vegetable Classification using Machine Learing, guides you through automating the sorting process, improving quality control, and enhancing efficiency in agriculture and food industries.

Banana Leaf Disease Detection leverages Deep Learning and Computer Vision to identify plant diseases early, helping farmers protect their crops, improve cultivation, and reduce losses with smarter, more sustainable farming methods.

Our project uses deep learning to detect leaf diseases from images. By training models like VGG16 and EfficientNet on a robust dataset, we accurately diagnose plant conditions, aiding farmers in early disease detection and promoting healthier crops.

Glaucoma Detection Using Deep Learning uses AI to find early signs of glaucoma in eye images. This helps doctors diagnose the disease quickly and prevent vision loss.

Our Blood Cell Classification project uses CNN, EfficientNetB4, and VGG16 models to correctly sort blood cell images. This reduces up and improves the accuracy of research, which helps doctors make better decisions.

Skin Cancer Detection project leverages advanced deep learning models, including CNN, DenseNet121, and EfficientNetB4, to accurately classify skin cancer images. This initiative aims to improve early diagnosis and patient outcomes.

Our Cervical Cancer Analysis project leverages the power of EfficientNetB0 to accurately classify different types of cervical cells. This initiative aims to enhance early cancer detection and improve patient outcomes through advanced AI technology.

Get a complete overview of Generative AI, covering models, use cases, benefits, limitations, tools, ethics, and industry applications.

Create advanced chatbots and AI projects with GPT-3.5-turbo and GPT-4. Perfect the art of designing human-like interactions using detailed code examples.

This project builds an XGBoost Regressor to predict healthcare costs, ensuring insurance profitability. We'll compare it with a linear regression baseline and learn to communicate results effectively to non-technical stakeholders.

This project shows how analytics and AI increase profit and reduce risk in player selection by using linear regression to predict performance for British Premier League football stars.

Learn to build real-time image classification models using Convolutional Neural Networks (CNN). This tutorial will guide you through creating and training models to predict insights for AI projects,

This project teaches Deep Neural Networks (DNNs) using a dataset of 86,000 businesses. Participants will learn key concepts and use Python libraries like pandas, numpy, and TensorFlow for data analysis, cleaning, model building, and tuning.

Diffusers and stable diffusion models can be used to improve image production. This project enables realistic synthesis with advanced deep learning techniques, interactive image creation via Gradio UI, and customizable training.

Question Answering system built on Pegasus+SQuAD for accurate responses. Optimized for high accuracy and user experience across applications

The Semantic Search System with Transformers and Faiss vectors can speed up and improve the accuracy of your searches. Find out about advanced information retrieval and personalized suggestions for a wide range of businesses.

Advanced transformer models and tokenization methods can be used to automate the summarization of documents. Quickly make high-quality abstracts to help people find knowledge and make decisions.

The goal of this project is to create a customer support chatbot by using advanced methods for natural language processing.

YOLOv8 is used in this project to identify human poses in real time. As the COCO dataset is used to train the model, its performance is checked, and poses in photos and videos are predicted. Pose recognition compresses the video output so that it can be s

YOLOv8 and OCR models are used for accurate and quick results in automated license plate identification and recognition.

You can improve UNet training by using checkpoints, LR adjustments, label encoding, and seeing examples to make sure they work.