Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Managing Editor, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, 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.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.
This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.