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Turn Python Scripts into Beautiful ML Tools
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| Amanda Moran |
To Production and Beyond: How to Manage the Machine Learning Lifecycle with MLflow
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What's Data Science Reporting?
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Introduction to Data Analysis with Python datatable
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| Anna Veronika Dorogush |
Gradient Boosting for data with both numerical and text features
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| Avik Das |
Dynamic programming for machine learning: Hidden Markov Models
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| Ben Fowler |
Evaluation of Traditional and Novel Feature Selection Approaches
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| Brad Rees |
RAPIDS: Open Source GPU Data Science
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| Christopher Ariza |
Fitting Many Dimensions into One: The Promise of Hierarchical Indices for Data Beyond Two Dimensions
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| Daniel J. Brooks |
Computer Vision with PyTorch
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| Dante Gama Dessavre |
Open Source is Better Together: GPU Python Libraries Unite
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| Dmitry Petrov |
Datasets and machine learning models versioning using open source tools
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| Eric Busboom |
Tackling Homelessness with Open Data
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| Fletcher Riehl |
Modeling Search Term Revenue: Using Embedding Layers to Manage High Cardinality Categorical Data
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| Franklin Sarkett |
Building a Data Driven Organization
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| Franklin Velasquez |
Introduction to H2O AutoML with Python
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| Hao Jin |
Accelerate your NumPy Data Science Workloads and Deep Learning Applications
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| Hareem Naveed |
Write the Docs!
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| Hayley Song |
Experimental Machine Learning with HoloViz and PyTorch in Jupyterlab
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| Ivona Tautkute |
AI meets Fashion for product retrieval with multi-modally generated data
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| James Powell |
What You Got Is What You Got
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| Jeffrey Mew |
Build an AI-powered Pet Detector in Visual Studio Code
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| John Healy |
MAP all the things
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| John Mount |
Preparing messy data for supervised learning with vtreat
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| Joseph Kearney |
Introducing Autoimpute: a Python Package for Grappling with Missing Data
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| Juan S Vasquez |
Web Scraping w BeautifulSoup & Yelp's API
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| Kevin Chrzanowski |
Bokeh Maps: Making an interactive map for your next web application
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| Leland McInnes |
Learning Topology: Topological Techniques for Unsupervised Learning
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| Malte Loller-Andersen |
Reinforcement Learning: Pac-Man
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| Manu Flores |
Analyzing genetic networks using neural networks
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| Manu Gopinathan |
Reinforcement Learning: Pac-Man
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| Maria Khalusova |
Machine Learning Model Evaluation Metrics
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| Matthew Seal |
Data and ETL with Notebooks in Papermill
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| Michelle Brenner |
Ahead in the Clouds: How to get started with serverless on Google, Amazon & Microsoft
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| Mike Lee Williams |
Federated learning
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| Nick Acosta |
IBM Code and Response: Open Sourcing Natural Disaster Preparedness and Relief
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| Nina Zumel |
Preparing messy data for supervised learning with vtreat
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| Pasha Stetsenko |
Introduction to H2O AutoML with Python,
Introduction to Data Analysis with Python datatable
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| Paul Anzel |
Git-ting along with others
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| Raul Maldonado |
A/B Testing in Python
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| Ravin Kumar |
Making data relevant to business. Its harder than you think!
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| Richard Liaw |
A Guide to Modern Hyperparameter Tuning Algorithms
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| Rodolfo Bonnin |
A "Supremely" Light introduction to Quantum Computing
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| Shahid Barkat |
Introducing Autoimpute: a Python Package for Grappling with Missing Data
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| Sujit Pal |
Building Named Entity Recognition Models Efficiently using NERDS
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| Tim Orme |
Simplicity For Scale: Analyzing 15 Million DNA Samples With Python
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| Tom Goldenberg |
Kedro + MLflow – Reproducible and versioned data pipelines at scale
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