Top Tutorials to Study - BKJackson/BKJackson_Wiki GitHub Wiki
Python Machine Learning
Simple Machine Learning Pipeline - Bringing together all essential parts to build a simple, but powerful Machine Learning pipeline. This will cover Keras/TensorFlow model training, testing, auto re-training, and REST API
ESAC stats 2014 with Jake Van Der Plas - Good basic frequentist vs Bayesian intro and GMM overview, among others. Also, YouTube Videos
Introduction to Deep Learning (I2DL)
Pydata talks on Youtube
Pydata London 2019 - Full playlist
The frustration of diversity efforts in STEM - Lorena A. Barba, July 18, 2019
Embeddings! Embeddings everywhere! - Maciej Arciuch, Karol Grzegorczyk, July 18, 2019
Productionising Data Science at Scale - Trevor Sidery, Guillermo Barquero
Understanding of distributed processing in Python - Imran Rashid
Deep Learning and Time Series Forecasting for Smarter Energy - Igor Gotlibovych
Metaflow
Some good algorithms and notebooks
Nicolas Holland's Various projects - some cool stuff
For Mode prediction
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Decision Optimization
Using Bayesian Decision Making to Optimize Supply Chains - Thomas Wiecki 2019
Execution of data science projects with DevOps - Feature, User story, Task, etc. for data science, Also covers good practices for a git branching model. Vincent Driessen's Git flow This has the command line git code examples.
Enabling CI/CD for Machine Learning project with Azure Pipelines - Entered Sept 3. 2019
Intro to DevOps - 30 min video, by Microsoft
Ray Project Tutorials - for reinforcement learning
Python tricks
Lambda, Map, and Filter in Python - Syntax and usage, May 6, 2017