Supervised Machine Learning methods (Random Forest and SGD Classifier) to classify short conversations extracted from Reddit
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Updated
Oct 18, 2017 - Python
Supervised Machine Learning methods (Random Forest and SGD Classifier) to classify short conversations extracted from Reddit
Binary Classification for Movie Reviews
A model that could accurately predict the Industry Domain for different start-ups and companies based on descriptions, titles and categories.
A simple classifier of music genders, Naive bayes, SGD and SVM used.
In this project, we will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause.
Contains code for a voting classifier that is part of an ensemble learning model for tweet classification (which includes an LSTM, a bayesian model and a proximity model) and a system for weighted voting
Training binary classifier and multi-class classifier to classify the MNIST datase
Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.
Using MNIST dataset and classifying images
Auto Classify Text
Recognize Handwritten Digits(persian/english) with Neural Networks
Text Category Classifier
The objective of this project is to classify whether upcoming product will have positive or negative Sentiment.
Final Year project based upon Network Intrusion Detection System
Perceptron, Pseudo Inverse Linear Classifier, SGD, SVM, Batch Gradient Descent, Minibatch Gradient Descent with tensorflow
Image Recognition using Python on MNIST dataset with the help of CNN, Multiclass Logistic Regression and SGD
Applying predictive models on "Employee Turnover" dataset.
Improving Performance for Distributed SGD using Ray
Sentimental analysis on IMDB Movies reviews using Machine Learning classifier (Logistic Regression, Naive Bayes, SVM) and Deep Learning Models (Deep Neural Network, RNN, CNN)
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