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adaptive-api

adventures-in-ml-code

This repository holds all the code for the site http://www.adventuresinmachinelearning.com

algorithmandds

amazon-fine-food-reviews-analysis

The famous Amazon fine food reviews dataset on Kaggle for text classification. I have performed sentiment analysis on the dataset using different techniques. Please see readme for details.

android-college-grades-management-system

CSE Third Year (Semester 5) Individual Project - Mobile Version (Android)

appdichat

Appdichat is mobile chatting application with many features. You can register and login with the application. Also you can send friend request to your friends and accept or decline the requests received. You can also chat in real time with your friends and also see a list of the people using the application. A user can also build his profile by setting his display profile picture and set the status. Also you can view the profile of all your friends and know the number of mutual friends. The application uses Java, XML and the Firebase realtime database.

awesome-deep-learning-papers

The most cited deep learning papers

awesome-embedding-models

A curated list of awesome embedding models tutorials, projects and communities.

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

awesome-project-ideas

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. New PR ☛☛☛

awesome-text-summarization

The guide to tackle with the Text Summarization

basic-guide-to-natural-language-processing-with-nltk-and-spacy

A basic guide to implement fundamental NLP techniques like text normalization, text similarity etc... through NLTK library and Spacy.

bike-sharing-demand-rmsle-0.3194-

Bike Sharing Demand.

calculator

A SIMPLE CALCULATOR APPLICATION IN ANDROID. I HAVE USED JAVA AND XML FOR THE LAYOUT. THE CALCULATOR HAS BASIC OPERATIONS AND SOME OTHER UTILITY FUNCTIONS.

cats-vs-dogs-cnn-keras

The famous Cats-vs-Dogs dataset. I have used a self laid ConvNet to classify the image into 2 classes either a Dog or a Cat. The images used are of 100*100 pixels each. The images are first converted to the numpy array of pixel values using the python ZipFile module. The images are then divided into the training ,cross-validation,testing set containing 20000 , 5000 , 12500 images respectively. Also I have used data augmentation technique to avoid chances of overfitting the model. Finally I achieved a decent accuracy of about 88 % on the validation set.

chb-mit

Machine learning on the CHB-MIT scalp EEG database

cnns-on-chb-mit

The project is about applying CNNs to EEG data from CHB-MIT to predict seizure

coding-interview-university

A complete computer science study plan to become a software engineer.

conversational-datasets

Large datasets for conversational AI

cpuschedulerapp

A small Android application for the Operating System Lab Project. The application implements a short term scheduler. The user enters the details of the various processes and then chooses one among the many options for the CPU scheduling algorithm. The application then displays the sequence in which the processes will be executed and also various other quantities like waiting time,turn around time for a process etc..

darknet

Convolutional Neural Networks

data-science-complete-tutorial

Notebooks to learn data science - Videos https://www.edyoda.com/course/1416

deep-learning-for-recommendation-systems

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

dialogflow-android-client

Android SDK for Dialogflow

eeg-emotion-classification

eegfeatures

EEG Features to be extract from raw data.

flower-recognition-kaggle-cnn-keras

The dataset is Flower Recognition on Kaggle. The dataset consists of 4232 images each of different pixel values. Each of the image can be classified into either of 5 types-> 'Daisy','Rose' etc... . I have trained Convolutional Neural Network written in Keras to predict the flower on the validation set. Also used ImageDataGenerator to augment the training set and avoid overfitting problem .

gender-recognition-by-voice-val.-acc.-0.9908-

The Gender Recognition by Voice dataset from kaggle. The dataset consists of 3168 voice samples each of which has 20 different acoustic properties and the target variable is the 'gender' or the 'label'. I have done exhaustive EDA to analyze the data and the underlying trends. Also the outliers have been detected and removed for better performance. I have also done significant feature engineering by adding couple of new relevant features. Also I have normalized the data for better performance. Lastly I have used many classification algos. from the scikit to predict the 'gender' from the voice sample. For me SVM gives highest accuracy of about a little more than 99.1 %.