There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list. We decided to share this resource with you, and so here are Udemy's top selling courses.

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services.

CREATIVITY: IT'S AT THE HEART OF WHO WE HUMANS ARE… WE HUMANS ARE SPECIAL, RIGHT? Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece? OVER SOME 40,000 YEARS, HUMAN CREATIVITY HAS EXPLODED – FROM DRAWINGS ON CAVE WALLS THROUGH THE GREAT ART OF CENTURIES TO COME…. NOW, SCIENTISTS -- AND ARTISTS –ARE ASKING CAN A ROBOT TRULY IMAGINE AN ORIGINAL MASTERWORK? COMPUTATIONAL CREATIVITY IS LEADING US TO ASK NEW QUESTIONS ABOUT HUMAN CREATIVITY. IS THIS ESSENTIAL HUMAN TRAIT TRULY UNIQUE? WILL ARTIFICIAL INTELLIGENCE BE A COMPETITOR? OR CAN IT BE A COLLABORATOR, HELPING US TOWARD STILL UNIMAGINED CREATIONS? SCHAEFER: My first guest is a member of Google Brain's Magenta team. He is currently working on neural network models of sound and music and recently produced a synthesizer that designed its own sounds. SCHAEFER: Also with us, is an Assistant professor at the University of Illinois at Urbana Champaign in the Dept. of Electrical and Computer Engineering. He focuses on several surprising creative domains including the culinary arts and fashion and the theoretical foundations of creativity. SCHAEFER: Also with us is an Associate Professor of psychological and brain science at Dartmouth College. He's interested in the neural basis of imagination and in the evolution of human creativity. A former research fellow at MIT's Media lab and artist in residence at Google, please welcome Sougwen Chung.

Today is International Women's Day, and we're celebrating by highlighting some of the leading ladies in AI, machine learning and deep learning who have spoken at RE•WORK Summits and dinners over the past 12 months. Whilst the technology industry is seeing more and more women in top roles, the gender imbalance is still clear. At RE•WORK, we're passionate about encouraging women and girls into STEM and are proud to host our series of dinners and our Podcast celebrating women in AI. Earlier this year we hosted the AI Assistants Summit in San Francisco, where 50% of our speakers were women. This was a fantastic showcase of diversity, and we are striving to have more and more female experts presenting at our Summits.

It used to be you could spend an afternoon drumming up a home movie with your little sister, soundtrack it with your favorite mixtape cuts, and upload it to the Internet for sharing without a care. What's an amateur-at-best musician to do? I may have marched on a collegiate snare line (and therefore understand rhythm, phrasings, and tempo), but my ability to create melody probably stopped with middle school recorder lessons. Luckily, we musically challenged filmmakers and podcasters now have robots. A number of high-profile AI composition initiatives have surfaced in recent years--perhaps most notably, Sony's Flow Machine released its debut album in January--and slowly but surely these tools are moving from the research labs and professional production studios into publicly available spaces.

From past few decades, our educational system has been slowly adapting itself to the new age of technology. With every day something new is innovating, everyone needs to accept the new change and learn about it. The biggest change that a mankind is right now facing is the new age of technology i.e. As the artificial intelligence is leading its way into the educational system as well it is becoming more important to incorporate the changes in the way the leaning is happening at higher education levels. A recent analysis of artificial intelligence market in U.S educational sector concluded that use of artificial intelligence in this sector has a compound growth rate of 47.5% throughout 2017-2021 forecasted periods.

Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task. Pseudo-tasks simulate the effect of training towards closely-related tasks drawn from the same universe. In a suite of experiments, pseudo-task augmentation is shown to improve performance on single-task learning problems. When combined with multitask learning, further improvements are achieved, including state-of-the-art performance on the CelebA dataset, showing that pseudo-task augmentation and multitask learning have complementary value. All in all, pseudo-task augmentation is a broadly applicable and efficient way to boost performance in deep learning systems.

We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, called the $stop$ action, that ends the current episode. After the $stop$ action is taken, the learner collects a terminal reward, and observes the costs and terminal rewards associated with each step of the episode. The goal of the learner is to maximize its cumulative gain (i.e., the terminal reward minus costs) over all episodes by learning to choose the best sequence of actions based on the feedback. First, we define an {\em oracle} benchmark, which sequentially selects the actions that maximize the expected immediate gain. Then, we propose our online learning algorithm, named {\em FeedBack Adaptive Learning} (FeedBAL), and prove that its regret with respect to the benchmark is bounded with high probability and increases logarithmically in expectation. Moreover, the regret only has polynomial dependence on the number of steps, actions and states. eMAB can be used to model applications that involve humans in the loop, ranging from personalized medical screening to personalized web-based education, where sequences of actions are taken in each episode, and optimal behavior requires adapting the chosen actions based on the feedback.

Machine Learning mostly requires the fundamental understanding of Linear Algebra, Statistics and Probability. While you can learn how to use all the advanced libraries to accomplish your ML tasks, once something breaks you won't be able to fix it. Even worse, you won't be able to understand any new studies being done in the field, since to understand them, you will need a somewhat deep understanding of mathematics. Also, you won't be able to conduct your own studies or play around with mathematical ML concepts. This being a very huge topic, it is somewhat hard to find good resources which explain the content properly.

There is new code to play around with for those interested in machine learning and space, a model that predicts hilarious search trends for sex site YouPorn, and another funny story about an ostensibly intelligent medical chatbot in New Zealand. Hunting exoplanets with ML – The machine learning code that a Google engineer and an astrophysicist used to detect exoplanets has been published online. Christopher Shallue, a senior software engineer at Google, and Andrew Vanderburg, a postdoctoral fellow studying astrophysics at the University of Texas, USA, discovered another planet lurking in the Kepler-90 system. It was a special find. Not only was it spotted using a convolutional neural network, but it meant that the Solar System was no longer the biggest planetary system found so far.