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Part 1 of this short series focused on the business analytics / BI / operational research aspects, see here. In this Part 2, you will find the most interesting machine learning and statistics articles that I wrote in the last few years, focusing on core technical aspects.

In Part 1 of this short series, I have included the most interesting articles that I wrote in the last few years. This part focuses on the business analytics / BI / operational research aspects.

In this issue of JAMA, Liu and colleagues1 provide a users' guide to reading clinical machine learning articles. Beyond a synopsis of selected concepts in modern machine learning, the authors elaborate step-by-step guidance for physicians seeking to evaluate this evidence with a critical eye. In an era when readers are bombarded with artificial intelligence in everyday life, from credit card fraud warnings and smartphones that anticipate their needs to life-like videos of people who do not actually exist, the sanity check provided by this article is most welcome.

Between Jan Dec 2018, we've compared nearly 22,000 Machine Learning articles to pick the Top 50 that can improve your data science skill for 2019. This is an extremely competitive list (50/22,000 or 0.23% chance), and carefully picks the most useful Machine Learning articles published for the past year. Mybridge AI evaluates the quality by considering popularity, engagement and recency, and other human factors. Tutorials are divided into 16 relevant groups (see below).

Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer. Here at SAS, we hear questions every day about machine learning: what it is, how it compares to other technologies, and why it matters. On the more technical end of the spectrum, we also hear questions about specific machine learning algorithms and how to use them. You will get all of your answers -- and more -- if you take the time to read the articles listed here, from definitions and comparisons to how-to tips on data science techniques. This isn't a comprehensive list, of course, but I tried to pick some favorite articles I've seen published over the last year, and I'm trying to list them here in order from introductory to more advanced.