I already have a BA in Linguistics (French and German language teacher), but I really want to go into NLP and am doing my MA in Computational Linguistics now (my first year is about to start). I've read "Speech and Language Processing", started to learn advanced math needed for NLP (stats, the theory of probability, linear algebra) and am doing a course "Python for everybody" on Coursera now. My next plan was to study NLP for Python and try to volunteer for NLTK. But when I skim Data Scientist or NLP positions on job websites, I always read something like "the Master's degree in Math or CS" in requirements. On the other hand, when I finish my MA, I'll be 28 already, and am not sure, that I can afford to get another degree. But I have a feeling, that all the interesting things connected with NLP are more about machine learning, programming and math, and this is what I really want to do in life.

That's the vision of Andrew Ng, a founder of the Google Brain deep learning project, and former head of AI at Baidu–a position he left in March–who is today announcing a set of five interconnected online courses on the subject. Participants in the "Deep Learning Specialization," available only through Coursera, will be steeped in neural networks, backpropagation, convolutional networks, recurrent networks, computer vision, natural language processing, and more. They'll get hands-on experience using the technology in healthcare, visual object recognition, music generation, language understanding, and other applications. "Today, if you want to learn deep learning, there are lots of people searching online, reading [dozens of] research papers, reading blog posts, and watching YouTube videos," Ng tells Fast Company. "I admire that, but I want to give people that want to break into AI a clear path of how to get there." Today, the major breakthroughs in the field are coming from the world's largest tech companies, which have in-house AI departments and are investing significantly in the field.

The incorporation of technology in our everyday lives has been made possible by the availability of data in enormous amounts. Data is drawn from different sectors and platforms including cell phones, social media, e-commerce sites, various surveys, internet searches, etc. However, the interpretation of vast amounts of unstructured data for effective decision making may prove too complex and time consuming for companies, hence, the emergence of Data Science. Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming.

In 2015, a poll of 200 senior corporate executives conducted by the National Robotics Education Foundation identified robotics as a major source of jobs for the United States. Indeed, some 81% of respondents agreed that robotics was the top area of job growth for the nation. Not that this should come as a surprise: as the demand for smart factories and automation increases, so does the need for robots. According to Nearshore Americas, smart factories are expected to add $500 billion to the global economy in 2017. In a survey conducted by technology consulting firm Capgemini, more than half of the respondents claimed to have invested $100 million or more into smart factory initiatives over the last five years.

Until the last 5 years or so, it was infeasible to uncover topics and emotions across the web without powerful computing resources. Engineers didn't have efficient methods to make sense of words and documents at a large scale. Now, with deep learning, we can convert unstructured text to computable formats, effectively incorporating semantic knowledge for training machine learning models. Harnessing the vast data troves of the digital world can help us understand people more directly, going beyond the limitations of collecting data points through measurements and survey results. Here's a glimpse into how we achieve this at MarianaIQ.

Lots of people will tell you they're nervous about the changes artificial intelligence will bring to the world, but Andrew Ng is confident it's all for the best. The former AI chief of Baidu and founder of Google Brain is on a mission to build what he calls an "AI-powered society" -- one where smart computers are as integral to businesses as electricity. And to bring about that future, Ng, now an adjunct professor at Stanford, will share what he knows best by teaching. Today, Ng is launching a new course on deep learning on Coursera, the online education site he co-founded. The syllabus will follow his popular machine learning course, which has attracted some 2 million enrollments since its launch in 2011.

Find out how to optimize your website to give your customers experiences that will have the biggest ROI for your business. Growing up, there's a good chance you heard the mantra "go to a good school, get a good job, and make lots of money." On the surface, that seems like sound advice. After all, college graduates, on average, earn almost $1 million more in their lifetimes than those with only a high school education. Perhaps you were encouraged to get a professional degree to land a high paying job like a doctor, dentist, lawyer or something similar.

If the next era of human progress is built using AI, who gets to engineer it? Who will have the coding skills to use the software for creating AI products, or even more importantly, the skills to write that software? In an attempt to make the answer to those questions "anyone who wants to," Andrew Ng is releasing a new set of courses teaching deep learning on Coursera, the online learning platform he co-founded in 2012. Coursera was originally set up to offer an online class in machine learning; deep learning is a variety of that, involving exceptionally large datasets. The original machine learning course attracted more than 2 million students, Ng tells MIT Tech Review.

Students at John Burroughs High School will soon learn how to create and develop their own video games for computers and smartphone apps using a new textbook. The textbook is "Learn Mobile Game Development in One Day Using GameSalad" by Jamie Cross, a Pennsylvania-based computer programmer, and it received approval Thursday night from the Burbank Unified school board. Thirty-six copies will be purchased at $36.99 each for a total of roughly $1,500, according to a staff report. Students enrolled in the introductory computer course called Digital Applications will use the textbook as supplemental material to the GameSalad software, a coding program that uses a "drag-and-drop platform" to teach the fundamentals of computer science. The material is geared toward students who lack prior game-development experience.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. You can find all the interviews here. We'll be posting them regularly on Robohub.