Karthikeyan Sankaran's repositories
awesome-rl
Reinforcement learning resources curated
deploying-machine-learning-models
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
devops-master-class
Devops Tutorial for Beginners Docker, Kubernetes, Terraform, Ansible, Jenkins and Azure Devops
mli-resources
Machine Learning Interpretability Resources
testing-and-monitoring-ml-deployments
Example project for the course "Testing & Monitoring Machine Learning Model Deployments"
categorical-encoding
A library of sklearn compatible categorical variable encoders
competitive-data-science
Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course
Cookbook
The Data Engineering Cookbook
cookbook-2nd-code
Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]
deploy-heroku
Deploy an Image Classification Model on Heroku
ds-cheatsheets
List of Data Science Cheatsheets to rule the world
featexp
Feature exploration for supervised learning
GitGitHubMasterClass
GitHub Master Class from Udemy
h2o-meetups
Presentations from H2O meetups & conferences by the H2O.ai team
h2o-tutorials
Tutorials and training material for the H2O Machine Learning Platform
h2o_tutorials
Slides and code examples for H2O tutorials at various events
insight
Repository for Project Insight: NLP as a Service
ipython-notebooks
A collection of IPython notebooks covering various topics.
jupyter
Jupyter metapackage for installation, docs and chat
katacoda-scenarios
Katacoda Scenarios
navigate-data-science
Code related to my talk on 'Navigating the Data Science World'
pandas-pipelines-custom-transformers
Material for Talk at PyData Seattle 2017
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
shap
A unified approach to explain the output of any machine learning model
ThinkBayes2
Text and code for the second edition of Think Bayes, by Allen Downey.
yellowbrick
Visual analysis and diagnostic tools to facilitate machine learning model selection.