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.
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.
This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.
This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.
These short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept.
Read this insightful interview with Bokeh's core developer, Bryan Van de Ven, and gain an understanding of what Bokeh is, when and why you should use it, and what makes Bryan a great fit for helming this project.
This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.
Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.
This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.
I was asked this question recently via LinkedIn message: "What advice would you give your younger data scientist self?" The best piece of advice I honestly think I can give is this: Forget about "data science."
Data Science Primer: Basic Concepts for Beginners
5 Free Resources for Getting Started with Deep Learning for Natural Language Processing
Machine Learning Applied to Big Data, Explained
What Advice Would You Give Your Younger Data Scientist Self?