After factoring job satisfaction, salary, and openings, Glassdoor ranked Data Science careers #1 (with a Job Score of 4.8/5) and determined the average salary of a Data Scientist to be $118,709. Moreover, McKinsey estimates that market demand for such talent will dramatically outpace the supply – leaving as many as 190,000 unfilled positions in 2018 (in the U.S. alone). Companies are scrambling to find those rare individuals with the ability to synthesize complex math, computer science, engineering, and creativity. Do a quick search of any job board, and you will see such positions posted (and re-posted) daily. INSOFE – a globally-recognized Data Science institute established to cultivate quantitative and Machine Learning skills, in tandem with Soothsayer Analytics – a US-based Data Science & Artificial Intelligence firm, are jointly offering an innovative educational experience that synthesizes world-class education with real-world experience.

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. You can apply to the degree program either before or after you begin the Specialization.

In the last two years, artificial intelligence (AI) has started to become a mainstream topic, but core aspects of AI, like machine learning and data science, have been around for quite some time, so the technology has had plenty of time to impact the industry already. According to a study by Tata Consultancy Services, more than 90% of companies in the energy, high tech, telecom, retail, and automotive industries use AI today. The company researched 13 industries globally and found that more than 80% of companies use AI in some capacity. "Beyond the IT function, artificial intelligence is most often used in customer service, sales, marketing, and finance," states the report. "In energy, 100% of companies use AI – the only industry in which every company is using it. Of the companies that don't use AI today, all expect to by 2020."

For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher …

Serengil received his MSc in Computer Science from Galatasaray University in 2011. He has been working as a software developer for a fintech company since 2010. Currently, he is a member of AI and Machine Learning team as a Data Scientist. His current research interests are Machine Learning and Cryptography. He has published several research papers about these motivations.

Intel AI Lounge – Unleashing the Potential for Everyone Panel at SXSW Intel Business Panelists Dr. Dawn Nafus, Intel, Lila Ibrahim, COO, Coursera and Pratool Bhartri, AI Graduate Student Ambassador, Intel discuss how Intel is engaging with scientists, students and developers to increase momentum behind broader adoption of AI solutions. Artificial intelligence innovations will bring benefits to multiple industries, and to society as a whole in the way we lead our everyday lives. AI will change our lives for the better as machines learn, reason, act and adapt -- transforming industries by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems. Intel can offer crucial technologies to drive the AI revolution, but ultimately we must work together as an industry -- and as a society -- to achieve the ultimate potential of AI. About Intel Business: Get all the IT info you need, right here.

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico. Here we revisit the problem of supervised learning in temporally coding multi-layer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three factor learning rule capable of training multi-layer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike-time patterns.

A prospective student, looking forward to major in English, calls the financial aid office of her dream college. After two rings, an automated voice asks her what does she need help with. On her inquisitiveness about the financial aid procedure, the voice on the other end runs her through the nuances of the process in a systematic manner. Despite the student's repeated queries regarding the same thing, there is no sign of tiredness or irritation in the voice. The student, eventually, hangs up satisfied and pleased with the agent's efficiency. Imagine the same happening during the peak season in colleges.

I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software (ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India - Not still cheap), availability of data, good books and MOOCs. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera