ravirajadrangi Goto Github PK

click_through_rate_prediction

client-retention-demo

An end-to end demonstration and integration verification test (IVT) for Spark on z/OS.

cloud-dataproc

cloud-metrics

Project to create usage statistics from IaaS such as OpenStack, Eucalyptus, and Nimbus

cloudera-training

Training exercises for Cloudera's Distribution for Hadoop

cloudmesh

clustermassinference

cmd3

cntk

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

coal-exploration

A first look at the coal data.

collective.upload

File upload widget with multiple file selection, drag&drop support, progress bars, client-side image resizing and preview images.

conda-recipes

build and test recipes for conda

configuring-a-rnn-model-for-the-neural-dude-algorithm.

convnets-for-visual-recognition-

convolution-neural-networks

convolutional-neural-networks

convolutional-neural-networks-for-visual-recognition

convolutional-neural-networks-for-visual-recognition2

couponpurchase

coursera-practical-machine-learning

Repo for Coursera.com online course: Practical Machine Learning

courseraml

I took Andrew Ng's Machine Learning course on Coursera and did the homework assigments... but, on my own in python because I love jupyter notebooks!

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

courses-1

fast.ai Courses

crawling

crawling web site collections

crawling-projects

Web scraping and automation using python

credit-card-fraud-detection

credit-card-fraud-detection-using-autoencoders-in-keras

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

credit-score

crime-data-analytics

To build a Machine Learning model which can predict the type of crime that might happen in future at a particular location, which in turn can be used to raise awareness among the people.

cs190.1x_scalable-machine-learning

Assignments of the course "CS190.1x Scalable Machine Learning": using PySpark and MLlib in Jupyter Notebooks.