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On Being Model-driven: Metrics and Monitoring

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and...

Clustering in R

This article covers clustering including K-means and hierarchical clustering. A complementary Domino project is available. Introduction Clustering is a machine learning technique that enables researchers and...

Understanding Causal Inference

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by...

Exploring US Real Estate Values with Python

This post covers data exploration using machine learning and interactive plotting. If interested in running the examples, there is a complementary Domino project available. Introduction Models...

Natural Language in Python using spaCy: An Introduction

This article provides a brief introduction to natural language using spaCy and related libraries in Python. The complementary Domino project is also available. Introduction This article...

HyperOpt: Bayesian Hyperparameter Optimization

This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the HyperOpt Python package. There is a complementary Domino...

Deep Reinforcement Learning

This article provides an excerpt "Deep Reinforcement Learning" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article includes an overview of reinforcement...

Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Fenner. The excerpt and...

Manual Feature Engineering

Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine Learning with Python...

Data Science, Past & Future

Paco Nathan presented, "Data Science, Past & Future", at Rev. This blog post provides a concise session summary, a video, and a written transcript. Session Summary...

Seeking Reproducibility within Social Science: Search and Discovery

Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search & Discovery” at Rev....

A Practitioner’s Guide to Deep Learning with Ludwig

Joshua Poduska provides a distilled overview of Ludwig including when to use Ludwig’s command-line syntax and when to use its Python API. Introduction New tools are...

Data Science at The New York Times

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find...

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