Use Python and NLTK to build out your own text classifiers and solve common NLP problems
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Updated
Jan 15, 2020 - Jupyter Notebook
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
This project implements a Convolutional Neural Network (CNN) binary classifier inspired by the TinyVGG architecture.
Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM
This repository contains some machine learning projects as a practise on machine learning course on Coursera for Prof. Andrew Ng from Stanford University.
A simple binary classification project which detects whether a mushroom is edible or not
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.
An image classifier that determines whether an image contains a dog or a cat.
Short project done for the course "Introduction to Machine Learning" @ UniTS.
Pipeline for mask detection over a Pi camera video feed.
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.
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
Explores classification methods to predict whether a student will answer a test question correctly
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.
Binary classification model using PyTorch to predict heart failure using clinical data.
Implementation of a Simple Perceptron (Simplest Neural network by Frank Rosenblatt) in C based on the example given example in the Veritasium video.
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