Obtaining meaningful results from the data set using the model trained with machine learning methods.

Detection and localization of Asian hornets with a CNN using PyTorch

#AI Tensorflow, Machine Learning and Building a data model to recognize object detection with Keras back-end. This a research work. This library is designed for everyone to learn fast.

This repository contains my practice of Introduction to Computer Vision and Image Processing lab notebooks.

Image classification for dogs and cats with VGG-16 using PyTorch. Model accuracy: 99.6%. Classification API included

For MLSA Workshop

implementation of using model.h5 on flask

Classificationbox Toolkit - a .Net wrapper for Machine Box's Classificationbox service and a CLI program using it to train a model and sort image folders

this project includes the model to predict the number of requests for bike rental according to the dataset.

Moving train on railway line with CSS3

Uilizing Tensorflow and openCV frameworks, we have created a Face Mask Detection Software Script.

This project focuses on fine-tuning a pre-trained RoBERTa model (benjamin/roberta-base-wechsel-swahili) for Named Entity Recognition (NER) in Swahili using the MasakhaNER dataset. The goal is to identify and classify named entities such as PERSON, LOCATION, ORGANIZATION, DATE, etc., in Swahili text.

Two-stream cnn models for action recognition on the UCF-101 dataset

in this project, a model is presented to identify and recognize customers who will leave the cycle of existing customers.

YOLOv8 for Object Detection. This model is trained for the custom data set. YOLOv8 is an Open Source SOTA model built and maintained by the Ultralytics team. You guys can use this model for your custom dataset. I tried to provide the model just like plug and play.

Keras pretrained models (VGG16, InceptionV3, Resnet50, Resnet152) + Transfer Learning for predicting classes in the Oxford 102 flower dataset

Classify UCF101 videos using one frame at a time with a CNN(InceptionV3)

Social Emotion Analysis | SEA

This uses machine learning to recognize faces to mark attendance.

A structured Python and ML learning path covering programming basics, data science libraries, visualization, and machine learning algorithms. Includes hands-on projects for practical experience in data preprocessing, model training, and deployment. Ideal for beginners to build real-world data analysis and predictive modeling skills.