Great Learning has witnessed massive demand from professionals for its program in Artificial Intelligence and Machine Learning. Great Learning has received nearly 2000 applications from mid-senior IT, BFSI and Analytics professionals for 40 seats. The first batch has a collective experience of over 520 man-years with an average experience of about 13 years, ranging from 6 to 21 years. Even very senior executives, with over 20 years of experience have joined the course. The program garnered the tremendous response from professionals in Bangalore with more than 2000 of them indicating their interest for enrolling for it.

Board games aren't quite as sexy as their digital counterparts, but the hobby has nevertheless seen a resurgence in recent years. NPD Group reported that board game sales in the U.S. grew by 28 percent in 2016, cafes dedicated to the hobby are cropping up all over the country (and the world), and three out of the top ten most-funded projects on Kickstarter are tabletop games. Much of this can be attributed not just to the increased desire for real-life social interaction, but the rise of good-quality titles that are far superior to outdated classics like Monopoly and Candyland. But there is one problem with all of these new and exciting games: You need to learn how to play them. Unlike video games, which you can figure out by jumping into a tutorial and pressing a few buttons, tabletop versions often require you to read the instructions.

Suppose you could merely imagine a computation, and a digital prostheses, an extension of your biological brain, would turn it into code that instantly realizes what you had in mind. Imagine looking at an image, dataset or set of equations and wanting to analyze and explore its meaning as an artistic whim or part of a scientific investigation. I don't mean you would use an existing software suite to produce a standard visualization, but rather you would make use of an extensive repository of existing code to assemble a new program analogous to how a composer draws upon a repertoire of musical motifs, themes and styles to construct new works, and tantamount to having a talented musical amanuensis who, in addition to copying your scores, takes liberties with your prior work, making small alterations here and there and occasionally adding new works of its own invention, novel but consistent with your taste and sensibilities. Perhaps the interaction would be wordless and you would express your objective by simply focusing your attention and guiding your imagination, the prostheses operating directly on patterns of activation arising in your primary sensory, proprioceptive and associative cortex that have become part of an extensive vocabulary that you now share with your personal digital amanuensis. Or perhaps it would involve a conversation conducted in subvocal, unarticulated speech in which you specify what it is you want to compute and your assistant asks questions to clarify your intention and the two of you share examples of input and output to ground your internal conversation in concrete terms. More than thirty years ago, Charles Rich and Richard Waters published an MIT AI Lab technical report [68] entitled The Programmer's Apprentice: A Research Overview. Whether they intended it or not, it would have been easy in those days for someone to misremember the title and inadvertently refer to it as "The Sorcerer's Apprentice" since computer programmers at the time were often characterized as wizards and most children were familiar with the Walt Disney movie Fantasia, featuring music written by Paul Dukas inspired by Goethe's poem of the same name

BigML is bringing the fourth edition of our Summer School in Machine Learning to Valencia. We will hold a two-day crash course ideal for business leaders, industry practitioners, advanced undergraduates, as well as graduate students, seeking a quick, practical, and hands-on introduction to Machine Learning to solve real-world problems.

The world of academia is becoming more personalized and convenient for students thanks to recent advancements in artificial intelligence (AI). The technology has numerous applications that are changing the way we learn, making education more accessible to students with computers or smart devices if they're unable to make it to class. Students aren't the only ones who benefit as AI is also helping to automate and speed up administrative tasks, helping organizations reduce the time spent on tedious tasks and increasing the amount of time spent on each individual student. A recent study from eSchool News discovered that the use of AI in the education industry will grow by 47.5% through 2021 as we move towards a more connected world. The technology's impact will exist anywhere from Kindergarten through higher education, offering the opportunity to create adaptive learning features with personalized tools to improve the student experience.

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

Machine Learning A-Z: Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves and SuperDataScience Team will teach you Machine Learning using Python & R. These Python videos will teach you everything related to Machine Learning. You will build an army of powerful Machine Learning models. Then you will combine them to solve any problem. You will be able to handle different topics like Reinforcement Learning, NLP and Deep Learning. Using the knowledge you gain, you will know which Machine Learning model to use depending on the problem.

This half-day workshop aims at exploring emerging research in the areas of linked data in robotics and Industry 4.0. Industry 4.0 is a collective term (created in Germany) for the technological concepts of cyber-physical systems, the Internet of Things and the Internet of Services, leading to the vision of the Smart Factory. Within a modular structured Smart Factory, cyber-physical systems monitor physical processes, and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and humans in real time. In addition, one of the aims in robotics is to build smarter robots that can communicate, collaborate and operate more naturally and safely.

Earlier this year, Mattel announced that it was partnering with Tynker to bring Barbie-themed coding lessons to young kids. As of today, six free coding experiences are now available as is a new STEM-themed doll -- Robotics Engineer Barbie. The lessons are geared towards beginners, kindergarten-aged and older, and aim to teach logic, problem-solving and the basics of coding. While they learn, kids can also take on different career roles alongside Barbie, including musician, astronaut, pastry chef, robotics engineer, farmer and beekeeper. "Our mission is to empower youth to become the makers of tomorrow through coding, and the Barbie brand is an ideal partner to help us introduce programming to a large number of kids in a fun, engaging way," Tynker CEO Krishna Vedati said in a statement.