There are 1 repository under binary-classifier topic.
Use Python and NLTK to build out your own text classifiers and solve common NLP problems
The uploaded codes help to classify emails into spam and non spam classes by using Support Vector Machine classifier.
Build intelligent applications that can interpret the human language to deliver impactful results
Access the Linear or RBF kernel SVM from OCaml using the R e1071 or svmpath packages
In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.
This repository contains some machine learning projects as a practise on machine learning course on Coursera for Prof. Andrew Ng from Stanford University.
Short project done for the course "Introduction to Machine Learning" @ UniTS.
Pipeline for mask detection over a Pi camera video feed.
An image classifier that determines whether an image contains a dog or a cat.
A novel ML-based binary classifier to tell viral and non-viral long reads apart in metagenomic samples.
A binary classifier to test whether an image belongs to the "hot dog" class or the "not hot dog" class, as seen on HBO's Silicon Valley.
Using machine learning and neural networks, use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
Explores classification methods to predict whether a student will answer a test question correctly
Implementation of a Simple Perceptron (Simplest Neural network by Frank Rosenblatt) in C based on the example given example in the Veritasium video.
Online Passive Aggressive Binary Classifier
MNIST Handwritten Digits Classification
Binary Linear Classifier - AI Supervised Algorithm
Silicon Valley inspired binary classifier to identify hot-dogs and not-hot-dogs. Source: https://www.youtube.com/watch?v=vIci3C4JkL0
Binary classifier that determines whether an image is of a cat or a dog.
Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM
This tool is meant to help select the applicants for funding with the best chance of success in their ventures. The information was used to create a binary classifier that can predict whether applicants will be successful if funded.
A neural network logistic regression model that distinguishes between two dog breeds.
A Binary Classifier based on the Cats Vs Dogs dataset served in Flask
Repository featuring my data science portfolio, showcasing projects completed during my training at Yandex.Practicum.
The ML task in this assignment is binary classification, stock market prediction, based on daily news dataset.
Creating and optimizing a binary classifier that predicts whether charity organizations will be successful if funded by a fictional foundation. Done for Rutgers University's Data Science Boot Camp.
A collection of very simple neural networks implemented in PyTorch.
Brain MRI Images for Brain Tumor Detection using convolutional neural networks.
Ameliorating Performance of Random Forest using Data Clustering | Research for a novel approach for binary class classification problem
Deep Learning using Python and Tensorflow 2.0 (binary classification)
Slovenian Definition Extraction
The primary objective of this project was to develop a predictive model capable of accurately determining whether a bank customer will maintain or close their account. Kaggle Playground Series S4E1
Create a binary classifier that is capable of predicting whether applicants will be successful if funded by a charity. This involved taking a deep dive into neural networks. Initially run on google-colab
Toy neural network in rust