Mastery of the productivity applications broadly used in business. Microsoft Imagine Academy curricula is organised along learning paths that guide students and educators to earning industry-recognised certifications and skills needed for jobs of tomorrow.

Manuela M. Veloso is the Herbert A. Simon University Professor in the School of Computer Science at Carnegie Mellon University. She is the Head of the Machine Learning Department, and researches in Artificial Intelligence. With her students, she researches with a variety of autonomous robots, including mobile service robots and soccer robots.

Machine learning is increasingly shaping future of work and jobs. With an average salary of $120,000 (Glassdoor and Indeed), Machine Learning will help you to get one of the top-paying jobs. Machine Learning, provides computers the ability to automatically learn and improve from experience. Today, data scientists are generally divided among two languages, some prefer R, some prefer Python. Learning Machine Learning is a definite way to advance your career and will open doors to new Job opportunities.

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.

Dormehl starts with the 1964 World's Fair -- held only miles from where I lived as a high school student in Queens -- evoking the anticipation of a nation working on sending a man to the moon. He identifies the early examples of artificial intelligence that captured my own excitement at the time, like IBM's demonstrations of automated handwriting recognition and language translation. He writes as if he had been there. Dormehl describes the early bifurcation of the field into the Symbolic and Connectionist schools, and he captures key points that many historians miss, such as the uncanny confidence of Frank Rosenblatt, the Cornell professor who pioneered the first popular neural network (he called them "perceptrons"). I visited Rosenblatt in 1962 when I was 14, and he was indeed making fantastic claims for this technology, saying it would eventually perform a very wide range of tasks at human levels, including speech recognition, translation and even language comprehension. As Dormehl recounts, these claims were ridiculed at the time, and indeed the machine Rosenblatt showed me in 1962 couldn't perform any of these things.

According to a report by BofA Merrill Lynch, the Robots and Artificial Intelligence solutions market will grow to $153 billion dollars by 2020 – comprising 83 billion dollars for robot and robotics and 70 billion dollars for AI-based analytics. As Venkat Viswanathan, chairman and founder at LatentView Analytics states, "What can be automated should be. In rule based processes, AI is more efficient than human interaction. However, we are far away from AI being center stage. I estimate that on an average about 90 percent of analysis today is done by humans and 10 percent by machines. As we build technology that helps machines get smarter, this will change. In another 10 years, machines will do 50 percent of analysis."

Artificial Intelligence is tipped to bring a tectonic shift in the world. People have debated over its usefulness in great lengths and detail. Today, the biggest fear is that AI will be a threat to jobs. And the alternative to that is to create a robust system of education. But, would it possible to use AI and train a breed of students that would be resistant to the ills of the technology?

Joe Greenwood is lead executive for data at MaRS Discovery District. As the end of the academic year approaches, thousands of graduates are about to enter a strong job market. According to the Royal Bank of Canada, Canada's economy is on target to add 2.4 million jobs over the next four years. But do our graduates have the skills they need to land these jobs? On paper, they are better prepared for work than their parents, with more holding advanced degrees.

Editor's note: This article is a shorter version of James Le's original, which you can find here in its entirety. Why is data science sexy? It has something to do with so many new applications and entire new industries come into being from the judicious use of copious amounts of data. Examples include speech recognition, object recognition in computer vision, robots and self-driving cars, bioinformatics, neuroscience, the discovery of exoplanets and an understanding of the origins of the universe, and the assembling of inexpensive but winning baseball teams. In each of these instances, the data scientist is central to the whole enterprise. He/she must combine knowledge of the application area with statistical expertise and implement it all using the latest in computer science ideas.

In 1998, The Truman Show told the story of a man whose life, unbeknownst to him, is a phenomenally elaborate reality television show. Every day and around the clock, every move made by the hapless Truman Burbank (Jim Carrey) is captured by a network of hidden cameras and broadcast live worldwide for the entertainment of millions. And then Truman begins to notice discrepancies. "Things that don't fit," he says, in the original script. Twenty years later, The Truman Show writer Andrew Niccol frequently experiences what he calls Trumanesque moments. Sloppy art direction and set design. And he doesn't mean anything happening on a set or on a screen. "There'll be a traffic jam, for instance, for no reason," Niccol says. "In my mind, the reason is actually that Christof"--the all-powerful, demiurge director of The Truman Show--"isn't ready at the next set. Or when you see someone out of context. And you realize, oh my god, that person was in the hospital scene. Today, we are all Truman, our ...