30-days-of-python

30 days of Python programming challenge is a step by step guide to learn Python programming language in 30 days.

adversarial-explainable-ai

💡 A curated list of adversarial attacks on model explanations

ai-simplest-network

The simplest form of an artificial neural network explained and demonstrated.

aix360

Interpretability and explainability of data and machine learning models

audio_speech_eval

A Streamlit application that allows you to compute various speech and audio metrics

awesome-interpretable-machine-learning

awesome-production-machine-learning

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

car-price-prediction

cme193-ipython-notebooks-lecture

CME 193 lecture on IPython, Pandas and Statsmodels

competitive-data-science

Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course

coswara-data

Data repository of Project Coswara

coughvid_feb2021

Refer --> https://zenodo.org/record/4498364#.YSzKdo4zaMp

cov_project

Contains code for Web Application of COVID Detection using cough recording

covid-19-faco-cnn

Implementation of a convolutional neural network used to identify wheezes and crackles in an audio file which is fed Mel-Spectrograms as inputs. During processing, audio clips are copied to 5 second long buffers, and are split into multiple segments if necessary, with zero padding added to fill the rest of the buffer. During training, Mel-Spectrograms are transposed and wrapped around the time-axis to help allow the network to learn to identify features occurring at arbitrary times within the recording. Data augmentation was employed in the form of audio stretching (speeding up and down) as well as Vocal Tract Length Perturbation, especially for the scarcer 'wheeze' and 'wheeze and crackles' classes. A one hot data labelling scheme was chosen as earlier attempts at using a multi-label scheme using a Sigmoid output layer resulted in poor training results (which in hindsight may have been caused by an excessively high learning rate). Currently, both the 'wheeze' and 'wheeze and crackles' classes pose the greatest challenge in the area of classification, and frequently produce false negatives, as indicated by the poor recall scores. Overall validation accuracy currently stands at roughly 70%.

covid-detection

Speech Emotion Recognition

covid19-cough-classification-webapp

A streamlit webapp for classifying COVID-19 through cough audio using deep learning.

covid19-sounds-kdd20

covid_app

data-science-books-1

data-science-complete-tutorial

For extensive instructor led learning

data-scientist-roadmap

Toturial coming with "data science roadmap" graphe.

data_audio

datacamp

DataCamp data-science courses

datacamp-projects

Notebooks of Datacamp projects

datasciencecoursera

Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.

dcase2018task2

Team NUDT code for DCASE2018Task2.

deadsimplespeechrecognizer

CNN based Minimal model for recognizing word

deep-learning

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-coursera-1

Deep Learning Specialization by Andrew Ng, deeplearning.ai.