A New, Interactive Approach to Learning Data Science

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

This repository is a related to all about Deep Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

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.

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

This project demonstrates the implementation of the Perceptron algorithm for binary classification tasks. It includes various advanced features such as data augmentation, feature engineering, and deep learning techniques to enhance model performance and robustness.

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.

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.

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

DECISION TREE CLASSIFIER - HYPER PARAMETER TUNING - Binary Classification

Implementing a model that can verify if two images belongs to same personality or not. Answer the question "Is this the claimed person?" It is a 1:1 matching problem i.e. given a face your task is to compare the candidate face to another and verify whether it is a match or not. My custom CNN model has achieved marvelous performance on the dataset.

The dataset for this competition (both train and test) was generated from a deep learning model trained on the Pulsar Classification.

Used Machine Learning to create a cryptocurrency classification system for my investment banking client who is interested in offering cryptocurrencies for its customers.

Text Classification Problem : Wrote a module to classify Amazon-Product Reviews as favourable/unfavourable. Achieved accuracy of 78% and an F1 score of .81 using Logistic Regression on a test-train split of 20%, where total records were around 50000.