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Matthew Mayo

Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

Speed Up Statistical Computations in Python with Cython
Python

Speed Up Statistical Computations in Python with Cython

Posted on July 8, 2025July 7, 2025 by Matthew Mayo

Cython is a superset of Python that supports static type declarations, which Cython translates into highly optimized C code, and which makes writing C extensions for Python as easy as Python itself.

Survival Analysis: Techniques for Analyzing Time-to-Event Data
Fundamentals

Survival Analysis: Techniques for Analyzing Time-to-Event Data

Posted on June 6, 2025June 8, 2025 by Matthew Mayo

Survival analysis, primarily used for modeling time-to-event data, provides an arsenal of statistical tools to estimate the expected time until one or more events of interest, such as death, failure, or attrition.

7 Statistical Concepts Machine Learning Engineers Misunderstand
Resources

7 Statistical Concepts Machine Learning Engineers Misunderstand

Posted on May 8, 2025May 7, 2025 by Matthew Mayo

Let’s break down seven statistical concepts that even seasoned machine learning engineers often trip over — and why getting them right matters more than you think.

Correlation, Causation, and Confounding: Decoding Hidden Relationships in Data
Fundamentals

Correlation, Causation, and Confounding: Decoding Hidden Relationships in Data

Posted on February 19, 2025February 18, 2025 by Matthew Mayo

In this discussion we will unpack some of the nuances between correlation, causation, and confounding, shedding light on common pitfalls, and offering best practices for hypothesis testing and data visualization.

Applications of SpaCy in Real-World Text Analytics Projects
Programming

Applications of SpaCy in Real-World Text Analytics Projects

Posted on February 17, 2025February 17, 2025 by Matthew Mayo

In this article, we will focus on practical use cases, showcasing how spaCy can be applied end-to-end in real-world scenarios.

Building Custom NLP Pipelines and Extending SpaCy’s Functionality
Python

Building Custom NLP Pipelines and Extending SpaCy’s Functionality

Posted on February 14, 2025February 14, 2025 by Matthew Mayo

In this article, we explore how to tailor spaCy to your specific needs by creating custom NLP pipelines and extending its functionality.

Text Preprocessing and Feature Engineering with spaCy
Python

Text Preprocessing and Feature Engineering with spaCy

Posted on February 12, 2025February 13, 2025 by Matthew Mayo

In this article, we’ll focus on how to prepare text data for machine learning and statistical modeling using spaCy.

Exploring Linguistic Features with spaCy
Python

Exploring Linguistic Features with spaCy

Posted on February 11, 2025February 10, 2025 by Matthew Mayo

In our next spaCy series article, we take a look at advanced linguistic processing, including detailed POS tagging, dependency parsing, and named entity recognition.

How to Run Tests in Parallel with pytest Using pytest-xdist
Programming

How to Run Tests in Parallel with pytest Using pytest-xdist

Posted on February 11, 2025January 28, 2025 by Matthew Mayo

As your Python projects grow, so does the time it takes to run your test suite.

Introduction to spaCy: Installation, Basic Concepts, and Text Processing
Python

Introduction to spaCy: Installation, Basic Concepts, and Text Processing

Posted on February 10, 2025 by Matthew Mayo

This will be the first in a series of articles on using spaCy for text analytics. In this first article, we’ll set the stage by introducing spaCy.

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