binaryclassification

A New, Interactive Approach to Learning Data Science

This repository offers video-based tutorials on Deep Learning concepts along with practical implementations using Python, TensorFlow, and Keras. It is designed for students, educators, and self-learners who want to understand the theory and apply it through hands-on projects.

Nudity/pornography detection using deeplearning. This model is trained using pretrained VGG-16. To know more about this check the readme file below

ML-powered Loan-Marketer Customer Filtering Engine

A computer vision-based waste identifier utilizes advanced image processing techniques and machine learning

This nuget package is designed to help you easily identify and detect profanity or bad words within a given sentence or string. It works under a simple binary classifier that has been built and trained using ML.NET for accurate and efficient detection of inappropriate language.

Бинарная классификация пользователей образовательной платформы Stepic на тех, кто скорее всего пройдет курс до конца и тех, кто скорее всего покинет платформу. Модель способна давать предсказания по анализу поведения юзеров на сайте за первые 3 дня

Designing your first machine learning pipeline with few lines of codes using Orchest. You will learn to preprocess the data, train the machine learning model, and evaluate the results.

Malaria is a serious global health problem that affects millions of people each year. One of the challenges in diagnosing malaria is identifying infected cells from microscopic images of blood smears. Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have been used for image classification tasks etc

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

A Binary Classification application to predict if a car price is good deal or not based on a set of attributes using FastTree trainer based on the Multiple Additive Regression Trees (MART).

Loan Default Prediction Model: A machine learning project that leverages historical lending data to create predictive models for assessing loan default risk, aiding financial institutions in making informed lending decisions.

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

EDA, pre-processing, train & test models, select best-performing model based on accuracy, precision, recall, and F1-score

Develop a technology for detecting mining sites using images from the optical satellite Sentinel-2. Specifically, it involves classifying images that contain mining sites and those that do not.

Binary Classification of mnist data using Stochastic Gradient Descent(SGD)

Classification of two varients of rice - Osmancik, and Cammeo using machine learning