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.

The Human Data Scientist: Safeguarding Your Career in the World of Automation

By Matthew Mayo, KDnuggets Managing Editor on February 28, 2017 in Advice, Automated Data Science, Career, Data Scientist
"Data scientist" continues to be recognized as a top career, but does this mean unending spoils for the data scientist? With large scale mass automation on the horizon for numerous professions, what can we do to safeguard our positions?
17 More Must-Know Data Science Interview Questions and Answers, Part 2

By Matthew Mayo, KDnuggets Managing Editor on February 22, 2017 in Algorithms, Data Science, Ensemble Methods, Feature Engineering, Feature Selection, High-dimensional, Interview Questions, Overfitting, Unsupervised Learning
The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.
5 Career Paths in Big Data and Data Science, Explained

By Matthew Mayo, KDnuggets Managing Editor on February 6, 2017 in Big Data, Career, Data Analyst, Data Engineering, Data Infrastructure, Data Science, Explained, Machine Learning
Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.
5 Free Courses for Getting Started in Artificial Intelligence

By Matthew Mayo, KDnuggets Managing Editor on February 1, 2017 in AI, Artificial Intelligence, Deep Learning, MIT, Reinforcement Learning, Self-Driving Car, UC Berkeley
A carefully-curated list of 5 free collections of university course material to help you better understand the various aspects of what artificial intelligence and skills necessary for moving forward in the field.
Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms

By Matthew Mayo, KDnuggets Managing Editor on January 25, 2017 in Algorithms, Machine Learning, Programming, Python
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
The Data Science Puzzle, Revisited

By Matthew Mayo, KDnuggets Managing Editor on January 20, 2017 in AI, Big Data, Data Mining, Data Science, Deep Learning, Machine Learning
The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.
The Current State of Automated Machine Learning

By Matthew Mayo, KDnuggets Managing Editor on January 18, 2017 in Automated, Automated Data Science, Automated Machine Learning, Hyperparameter, Machine Learning
What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.
5 Machine Learning Projects You Can No Longer Overlook, January

By Matthew Mayo, KDnuggets Managing Editor on January 2, 2017 in Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
Data Science Basics: Power Laws and Distributions

By Matthew Mayo, KDnuggets Managing Editor on December 21, 2016 in Beginners, Data Science, Distribution, Zipf's Law
Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.
Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017

By Matthew Mayo, KDnuggets Managing Editor on December 20, 2016 in 2017 Predictions, AI, Artificial Intelligence, Machine Learning, Predictions
As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.

Matthew Mayo

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