AI PROJECTS SOURCE CODE

Optimize document retrieval with GPT-4, using vector indexing and chunk size tuning for fast, accurate real-time and real-world AI search insights.

Corrective Retrieval-Augmented Generation (RAG) enhances response accuracy by dynamically adjusting the retrieval process, ensuring relevant, up-to-date information.

Improve document retrieval with contextual overlapping windows, PDF processing, text chunking, FAISS, and OpenAI embeddings for more coherent search results.

Improve document retrieval with OpenAI's GPT-4 and FAISS, generating context-based questions and accurate answers for efficient processing and information extraction from PDFs.

Optimize document retrieval with AI using FAISS, OpenAI embeddings & context windows for smarter knowledge management & Q&A systems.

GraphRAG is a document retrieval system that combines vector search, knowledge graph traversal and LLMs for accurate, context-aware query responses.

Efficient document retrieval system using FAISS, DeepSeek and LangChain, generating accurate answers and quick access to relevant information.

AI-driven document retrieval system using FAISS, BM25 and LLMs for fast, accurate search in legal, academic, corporate and research applications with citations.

Deep learning-based brain tumor detection using Mask R-CNN for accurate segmentation, aiding early diagnosis and assisting healthcare professionals.

Learn how to create a personalized movie recommendation system using SVD, LightFM, collaborative filtering, and weighted ratings for accurate and scalable suggestions.

A powerful face recognition system leveraging MTCNN for detection and InceptionResnetV1 for embedding extraction, offering reliable face matching and similarity detection.

Forecast healthcare call volumes using Prophet with enhanced features from Cesium. Improve accuracy with statistical features and seasonal patterns.

Learn how to classify human actions from images using deep learning models like ResNet50 and InceptionV3 for security, healthcare and smart home applications.

This project forecasts stock market volatility using ARCH and GARCH models, helping traders and investors predict market changes and manage financial risks effectively.

Analyze Vanilla GAN vs. WGAN for MNIST image generation, using FID and Inception Score to evaluate and compare the quality of generated images.

Use Multimodal RAG to extract, summarize, and analyze research papers! AI-powered image & text processing with GPT-4o for advanced academic insights.

Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions

Multi-class text classification using RNN and LSTM for analyzing customer complaints, and providing real-world business insights and solutions.

Build and assess time series forecasting models such as ARIMA, ARIMAX, and SARIMAX using real-world data from sectors like Healthcare and Banking for precise predictions.

Clean and analyze IoT sensor data by building and evaluating MA and AR models, using RMSE and visualizations to determine the best forecasting model.

Understand time series forecasting with Linear Regression, ARIMA, and anomaly detection. Learn feature engineering, trend analysis, and model evaluation effectively.

Develop a hybrid recommendation system using collaborative and content-based filtering with LightFM for personalized product recommendations based on customer behavior.

Predict Healthcare trends using Gaussian Process Regression. Understand preprocessing, modeling, and evaluation techniques for precise time-series forecasting results.

Classify mental health statements using NLP and ML techniques. Includes preprocessing, TF-IDF, XGBoost, Random Over-Sampling, and real-world prediction applications.

Everyone understands the fact that a language is made up of words. Moreover, combining them appropriately is essential in many intricate activities such as natural language processing (NLP) and machine learning.

Get hands-on experience in NLP with a project focused on text processing and supervised classification.

Analyze customer reviews with NLP, sentiment analysis, topic modeling, and K-Means clustering to uncover trends, and insights and improve business strategies.

Preprocess data, engineer features, and apply models like Random Forest or XGBoost. Evaluate performance and use LIME/SHAP for interpretability.

Build a model to predict house prices using Linear Regression. Understand data cleaning, feature selection, and model evaluation for accurate price forecasts.

Predict customer churn with Decision Trees! Learn data cleaning, SMOTE, and model evaluation using Python. Compare Decision Tree and Logistic Regression models to find the best approach in this hands-on, beginner-friendly project.

Build and evaluate regression models (Linear, Lasso, Ridge) to predict laptop prices with effective data preprocessing and performance metrics.

Understand how CBOW, Skip-Gram, and FastText models capture word meanings, visualize embeddings, and evaluate model performance for various NLP tasks.

Learn retail sales prediction with machine learning. This project builds regression models to analyze sales trends and drive data-driven decisions for retail success.

Accurate loan eligibility prediction with machine learning, applying SMOTE, data processing, and Random Forest for fair and efficient credit decisions.

Learn how to implement polynomial regression to capture complex patterns in data. Discover applications in finance, healthcare, and forecasting with detailed insights and methods.

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.