About Matthew Mayo

Matthew Mayo is a Data Scientist and Editor of KDnuggets, the seminal online data science resource, as well as a machine learning aficionado and an all-around data enthusiast. He is particularly interested in unsupervised learning, deep neural networks, natural language processing, algorithm design and optimization, and distributed approaches to data processing and analysis. Matthew holds a Master's degree in CS and a graduate diploma in Data Mining. Email him at mattmayo at kdnuggets[dot]com.

Matthew Mayo Posts (195)

  • Python Data Preparation Case Files: Group-based Imputation - 25 Sep 2017
    The second part in this series addresses group-based imputation for dealing with missing data values. Check out why finding group means can be a more formidable action than overall means, and see how to accomplish it in Python.
  • Gold Blog, Sep 201730 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - 22 Sep 2017
    This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
  • Silver Blog, Sep 20175 Machine Learning Projects You Can No Longer Overlook – Episode VI - 20 Sep 2017
    Deep learning, data preparation, data visualization, oh my! Check out the latest installation of '5 Machine Learning Projects You Can No Longer Overlook' for insight on... well, what machine learning projects you can no longer overlook.
  • Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks - 18 Sep 2017
    In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
  • Python Data Preparation Case Files: Removing Instances & Basic Imputation - 14 Sep 2017
    This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.
  • Neural Network Foundations, Explained: Activation Function - 13 Sep 2017
    This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.
  • Silver Blog, Sep 2017277 Data Science Key Terms, Explained - 01 Sep 2017
    This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.
  • Learning Machine Learning… with Flashcards - 31 Aug 2017
    Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.
  • Gold Blog, Aug 201742 Steps to Mastering Data Science - 25 Aug 2017
    This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
  • Deep Learning and Neural Networks Primer: Basic Concepts for Beginners - 18 Aug 2017
    This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.