Many scientific fields -- including biology, health, education, and the social sciences -- use machine learning (ML) to help them analyze data at an unprecedented scale. However, ML researchers who develop advanced methods rarely provide detailed tutorials showing how to apply these methods. Existing tutorials are often costly to participants, presume extensive programming knowledge, and are not tailored to specific application fields. In an attempt to democratize ML methods, we organized a year-long, free, online tutorial series targeted at teaching advanced natural language processing (NLP) methods to computational social science (CSS) scholars. Two organizers worked with fifteen subject matter experts to develop one-hour presentations with hands-on Python code for a range of ML methods and use cases, from data pre-processing to analyzing temporal variation of language change. Although live participation was more limited than expected, a comparison of pre- and post-tutorial surveys showed an increase in participants' perceived knowledge of almost one point on a 7-point Likert scale. Furthermore, participants asked thoughtful questions during tutorials and engaged readily with tutorial content afterwards, as demonstrated by 10K~total views of posted tutorial recordings. In this report, we summarize our organizational efforts and distill five principles for democratizing ML+X tutorials. We hope future organizers improve upon these principles and continue to lower barriers to developing ML skills for researchers of all fields.

Artificial intelligence (AI) and machine learning are no longer a secret weapon reserved for larger banks with deep pockets. Smaller financial institutions – including community banks and credit unions – are also finding opportunities to level the innovation playing field and implementing advanced solutions that can help FIs enhance customer experiences, stay a step ahead of fraudsters, and improve workflows. Read the report to learn more! We care about protecting your data.

H2O.ai is based in Mountain View, California and offers a suite of Machine Learning platforms. H2O's core strength is its high-performing ML components, which are tightly integrated. H2O.ai is a Visionary in the Gartner Magic Quadrant for Data Science Platforms in its report released in Jan'2019. Let's take a brief look at the offerings of H2O.ai: H2O is an open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms and also has an AutoML functionality.

I am a huge fan of not doing work. If there is ever anything I can do to remove difficult or high-friction things from my professional or personal life, then I will do it. I also have an insatiable scientific curiosity and I love exploring things like machine learning. Learning these new things doesn't feel like work to me, it feels like breathing fresh air. I love the beauty and elegance of the math behind how some of these models work and can be trained.

This is a guest post by Erik Meijer (@headinthebox). He is an accomplished programming-language designer who runs the Cloud Programmability Team at Microsoft and a professor of Cloud Programming at TUDelft. There is a lot of hype and mystique around Machine Learning these days. The combination of the words "machine" and "learning" induces hallucinations of intelligent machines that magically learn by soaking up Big Data and then both solving world hunger and making us rich while we lay on the beach sipping a cold one. However, just as normal programmers can write code without needing to understand Universal Turing Machines, power domains, or predicate transformers, we believe that normal programmers can use Machine Learning without needing to understand vectors, features, probability density, Jacobians, etc.

It used to be that one great technology defined an era. The steam engine, for example, served as the catalyst for the rise of the industrial age. Nowadays, however, a number of amazing technical advances and inventions are contending for bragging rights as the leading technology of our times. I would argue that one is particularly worthy of such boasting: machine learning. Although it has been in slow and steady development for years and has been used in a few enterprise applications, it has recently burst onto the scene in response to the explosion of data in today's increasingly connected digital world.