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 (299)

• 10 Free Top Notch Natural Language Processing Courses - 07 Oct 2019
  
  Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics.
• 10 Great Python Resources for Aspiring Data Scientists - 09 Sep 2019
  
  This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.
• Is Kaggle Learn a “Faster Data Science Education?” - 20 Aug 2019
  
  Kaggle Learn is "Faster Data Science Education," featuring micro-courses covering an array of data skills for immediate application. Courses may be made with newcomers in mind, but the platform and its content is proving useful as a review for more seasoned practitioners as well.
• 12 NLP Researchers, Practitioners & Innovators You Should Be Following - 12 Aug 2019
  
  Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more.
• Data Science: Scientific Discipline or Business Process? - 08 Aug 2019
  
  Simply put, data science is an attempt to understand given data using the scientific method. That's why data science is a scientific discipline. You are free (and encouraged!) to apply data science to business use cases, just as you are encouraged to apply it to many other domains.
• 25 Tricks for Pandas - 06 Aug 2019
  
  Check out this video (and Jupyter notebook) which outlines a number of Pandas tricks for working with and manipulating data, covering topics such as string manipulations, splitting and filtering DataFrames, combining and aggregating data, and more.
• Fantastic Four of Data Science Project Preparation - 26 Jul 2019
  
  This article takes a closer look at the four fantastic things we should keep in mind when approaching every new data science project.
• Things I Learned From the SciPy 2019 Lightning Talks - 22 Jul 2019
  
  This post summarizes the interesting aspects of the Day One of the SciPy 2019 lightning talks, a flash round of a dozen ~3 minute talks covering a wide variety of topics.
• Practical Speech Recognition with Python: The Basics - 09 Jul 2019
  
  Do you fear implementing speech recognition in your Python apps? Read this tutorial for a simple approach to getting practical with speech recognition using open source Python libraries.
• State of AI Report 2019 - 05 Jul 2019
  
  This year's "State of AI Report" has been released. Read it to find out about the latest in AI research, talent, industry, and politics form the past 12 months.