I read a bunch of other articles on the subject, all of which helped and supported me on the path to passing the exam. After finishing I wanted to contribute to that pool of knowledge so, in true data science fashion, I thought I'd go ahead and open source my experience and preparatory materials. Everyone approaches these exams differently, so before I get into the meat of the article, it may help to detail my background and where I started studying for the exam. At the time I began studying, I had been practicing data science for approximately 7 years. I held a masters in applied statistics and did a minor in math during my undergraduate years.

Given any text prompt, the API will return a text completion, attempting to match the pattern you gave it. You can "program" it by showing it just a few examples of what you'd like it to do; its success generally varies depending on how complex the task is. The API also allows you to hone performance on specific tasks by training on a dataset (small or large) of examples you provide, or by learning from human feedback provided by users or labelers. We've designed the API to be both simple for anyone to use but also flexible enough to make machine learning teams more productive. In fact, many of our teams are now using the API so that they can focus on machine learning research rather than distributed systems problems.

At the end of 2019, BlueDot, a Toronto-based company specializing in infectious disease surveillance using artificial intelligence and machine learning, let its customers know about the Wuhan coronavirus outbreak in China. That was a full week ahead of the U.S. Center for Disease Control and Prevention, which let Americans know on Jan. 6, and nine days ahead of the World Health Organization on Jan. 9. More than 100 people have died from the coronavirus worldwide. While the virus continues to spread, BlueDot was able to prevent its customers from exposing themselves to any issues before the outbreak had left China and traveled by plane to North America. BlueDot founder Kamran Khan founded the company in 2014 after seeing first hand what the SARS epidemic did to Toronto a decade earlier.

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.

Big numbers are nothing new to Quizlet, one of the most widely-used digital study tools in the United States. And according to one website tracker, it is among the most visited sites in the country (besting LinkedIn and Spotify). But there's another number that also has the company excited. This week Quizlet raised an additional $20 million in a Series B round led by Icon Ventures. Other investors include Union Square Ventures, Costanoa Ventures, Owl Ventures and Altos Ventures.