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.

Text Data Preprocessing: A Walkthrough in Python

By Matthew Mayo, KDnuggets Managing Editor on March 26, 2018 in Data Preparation, Data Preprocessing, NLP, Python, Text Analytics, Text Mining
This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools.
Top 12 Essential Command Line Tools for Data Scientists

By Matthew Mayo, KDnuggets Managing Editor on March 21, 2018 in Data Exploration, Data Science, Data Science Tools
This post is a short introductory overview of 12 Unix-like operating system command line tools of value to data science tasks, and the data scientists who perform them.
Quick Feature Engineering with Dates Using fast.ai

By Matthew Mayo, KDnuggets Managing Editor on March 16, 2018 in fast.ai, Feature Engineering, Machine Learning, Pandas, Python, Time Series
The fast.ai library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.
5 Things to Know About Machine Learning

By Matthew Mayo, KDnuggets Managing Editor on March 7, 2018 in Accuracy, Data Preparation, Ensemble Methods, Google Colab, Jupyter, Machine Learning, Validation
This post will point out 5 thing to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten.
5 Fantastic Practical Natural Language Processing Resources

By Matthew Mayo, KDnuggets Managing Editor on February 22, 2018 in Deep Learning, Keras, LSTM, Neural Networks, NLP, NLTK, Python
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
Data Science at the Command Line: Exploring Data

By Matthew Mayo, KDnuggets Managing Editor on February 14, 2018 in Data Exploration, Data Science, Data Science Tools
See what's available in the freely-available book "Data Science at the Command Line" by digging into data exploration in the terminal.
3 Essential Google Colaboratory Tips & Tricks

By Matthew Mayo, KDnuggets Managing Editor on February 12, 2018 in Google, Google Colab, Python, TensorFlow, Tips
Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.
5 Machine Learning Projects You Should Not Overlook

By Matthew Mayo, KDnuggets Managing Editor on February 8, 2018 in Bayesian, Gradient Boosting, Keras, Machine Learning, Overlook, PHP, Python, scikit-learn
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
5 Fantastic Practical Machine Learning Resources

By Matthew Mayo, KDnuggets Managing Editor on February 6, 2018 in Deep Learning, fast.ai, Gluon, Machine Learning, MOOC, MXNet, Python
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.
Using AutoML to Generate Machine Learning Pipelines with TPOT

By Matthew Mayo, KDnuggets Managing Editor on January 29, 2018 in Automated Machine Learning, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.

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