About Matthew Mayo

Matthew Mayo is Machine Learning Researcher and the Editor of KDnuggets, the seminal online Data Science and Machine Learning resource. 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 (288)

• The Machine Learning Puzzle, Explained - 17 Jun 2019
  
  Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.
• The Infinity Stones of Data Science - 10 Jun 2019
  
  Do you love data science 3000? Don't want to be embarrassed in front of the other analytics wizards? Aspire to be one of Earth's mightiest heroes, like Kevin Bacon? Help make data science a snap with these simple insights.
• 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - 03 Jun 2019
  
  This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
• When Too Likely Human Means Not Human: Detecting Automatically Generated Text - 23 May 2019
  
  Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
• 7 Steps to Mastering SQL for Data Science — 2019 Edition - 17 May 2019
  
  Follow these updated 7 steps to go from SQL data science newbie to practitioner in a hurry. We consider only the necessary concepts and skills, and provide quality resources for each.
• Top Data Science and Machine Learning Methods Used in 2018, 2019 - 29 Apr 2019
  
  Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.
• Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - 09 Apr 2019
  
  Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
• Another 10 Free Must-See Courses for Machine Learning and Data Science - 05 Apr 2019
  
  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.
• What Does GPT-2 Think About the AI Arms Race? - 01 Apr 2019
  
  It may be April first, but that doesn't mean you will necessarily be fooled by GPT-2's views on the AI arms race. Why not have a read for fun and to see what the language generation model is capable of.
• Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - 07 Mar 2019
  
  CRM/Consumer analytics, health care, banking, finance, and science were the top sectors in 2018. The greatest increases were in mobile apps, investment, security, entertainment, and social policy, while fraud detection, retail, advertising, direct marketing, and social media saw the greatest declines.