Matthew Mayo

KDnuggets Managing Editor

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.

Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera

By Matthew Mayo, KDnuggets Managing Editor on November 4, 2016 in DeepMind, Geoff Hinton, Image Recognition, Machine Learning, Neural Networks, Reddit, Self-Driving Car
NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car
Decision Tree Classifiers: A Concise Technical Overview

By Matthew Mayo, KDnuggets Managing Editor on November 3, 2016 in Algorithms, C4.5, CART, Decision Trees
The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
Automated Machine Learning: An Interview with Randy Olson, TPOT Lead Developer

By Matthew Mayo, KDnuggets Managing Editor on October 28, 2016 in Automated Data Science, Automated Machine Learning, Machine Learning, Python, scikit-learn
Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.
Frequent Pattern Mining and the Apriori Algorithm: A Concise Technical Overview

By Matthew Mayo, KDnuggets Managing Editor on October 27, 2016 in Algorithms, Apriori, Association Rules, Frequent Pattern Mining
This post provides a technical overview of frequent pattern mining algorithms (also known by a variety of other names), along with its most famous implementation, the Apriori algorithm.
5 EBooks to Read Before Getting into A Machine Learning Career

By Matthew Mayo, KDnuggets Managing Editor on October 21, 2016 in Bayesian, Data Science, Deep Learning, Free ebook, Machine Learning, Reinforcement Learning
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware

By Matthew Mayo, KDnuggets Managing Editor on October 7, 2016 in Canada, Courses, Deep Learning, Generative Models, Geoff Hinton, Machine Learning, Reddit, xkcd
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team

By Matthew Mayo, KDnuggets Managing Editor on October 4, 2016 in Automated, Automated Data Science, Automated Machine Learning, Competition, Machine Learning, scikit-learn
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
Data Science Basics: Data Mining vs. Statistics

By Matthew Mayo, KDnuggets Managing Editor on September 28, 2016 in Beginners, Data Mining, Statistics
As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.
Comparing Clustering Techniques: A Concise Technical Overview

By Matthew Mayo, KDnuggets Managing Editor on September 26, 2016 in Algorithms, Clustering, K-means, Machine Learning
A wide array of clustering techniques are in use today. Given the widespread use of clustering in everyday data mining, this post provides a concise technical overview of 2 such exemplar techniques.
Data Science Basics: 3 Insights for Beginners

By Matthew Mayo, KDnuggets Managing Editor on September 22, 2016 in Algorithms, Beginners, Datasets, Overfitting, Supervised Learning, Unsupervised Learning
For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.

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