Data Science Blog | Practical Techniques (2)

The Curse of Dimensionality

Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings Danger of Big Data Big data is the rage. This could be lots of rows (samples)...

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Providing fine-grained, trusted access to enterprise datasets with Okera and Domino

Domino and Okera - Provide data scientists access to trusted datasets within reproducible and instantly provisioned computational environments. In...

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The Importance of Structure, Coding Style, and Refactoring in Notebooks

Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like...

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Data Drift Detection for Image Classifiers

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in...

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Model Interpretability: The Conversation Continues

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn...

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Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider...

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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,...

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Clustering in R

This article covers clustering including K-means and hierarchical clustering. A complementary Domino project is available. Introduction Clustering...

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Understanding Causal Inference

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

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Time Series with R

This article delves into methods for analyzing multivariate and univariate time series data. A complementary Domino project is available. ...

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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...

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Natural Language Processing 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...

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HyperOpt: Bayesian Hyperparameter Optimization

This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the...

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Deep Reinforcement Learning

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

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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....

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Deep Learning Illustrated: Building Natural Language Processing Models

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt "Natural Language Processing" from the book, Deep Learning...

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