gondin's repositories
benson_team_awesome
Project 1 for MetisBootcamp
email-sherlock
:mag_right: Email Sherlock identifies and analyzes clusters in large email datasets, which can be used to aid email-based investigations, and possibly, prevent similar cases, by identifying email that contains sensitive or classified information.
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
awesome-public-datasets
An awesome list of high-quality open datasets in public domains (on-going).
challenge-collecton
Solution to a collection of data science take home challenges.
clinton-email-cruncher
Download Hillary Clinton's emails and query them with sqlite
handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
incubator-mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
jgondin2.github.io
Build a Jekyll blog in minutes, without touching the command line.
late-night-revelers
Identified subway stations with the highest traffic between the hours of 10 PM to 5 AM for possible food vendors to tap into the market of "late night revelers" or evening shift workers. Techniques: exploratory data analysis using Pandas, data visualization using Tableau.
LisbonDataScienceMeetup
Code and notebooks from Lisbon Data Science Meetup
machine_learning_examples
A collection of machine learning examples and tutorials.
mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
predict-water-pump-failure
:potable_water: Predict the failure of waterpoints throughout Tanzania. Identify the main features that contributes to pump failures.
scikit-learn
scikit-learn: machine learning in Python
tensorflow
Computation using data flow graphs for scalable machine learning
TensorFlow-Examples
TensorFlow Tutorial and Examples for beginners
factCC
Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper
ThinkStats2
Text and supporting code for Think Stats, 2nd Edition