Shortening product life cycles and constant evolution of international products and technologies are posing major challenges to businesses. Not just start-ups, but established businesses too are having to constantly evolve for keeping up with new trends in products, services and technologies. With innovation becoming a buzz word, going back to school to keep up with, if not go ahead of the curve, is seeing top managers and CEOs undertaking courses to acquire new skills-- 'retooling', in corporate jargon. "Typically, people who have reached the corner office in large organisations would have done their MBAs in the 1970s and 1980s," says Dr Farhan Pettiwala, president of Enactus India, a subsidiary of Enactus Worldwide, an international non-profit organisation of students with a focus on community empowerment through promotion of entrepreneurship. "They are extremely good at people skills, but struggle with technology, especially when it comes to the Internet of Things and Artificial Intelligence. So this aspect is missing in Indian education."

About this course: This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.

I bought a train ticket online from Virgin Trains recently, to get me to St. Neots, nearest station to the site of this conference, where I'm speaking to an association of freelance editors. The follow-up email from Virgin Trains surprised me. The subject line said: "Your St. Neots journey, your way." Your St. Neots journey is a well-formed English noun phrase using the proper name St. Neots as an attributive modifier of the noun journey. Trivial to program: They simply had to take Your _____ journey and substitute into the blank the name of the destination I had picked.

I am a data scientist, big data engineer, and full stack software engineer. I have a masters degree in computer engineering with a specialization in machine learning and pattern recognition. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

This course aims to put the entire world of machine learning with AWS in front of you. Machine learning has become the new black. Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge.

As unmanned technology develops at lightning speed and news feeds increasingly feature reports of drones encroaching on U.S. airspace and sometimes even commercial aircraft, airports across the country urgently need guidance, tools and a road map to provide users and customers with a safe, secure operating environment. Brent Terwilliger is developing solutions. Terwilliger – an Embry-Riddle Aeronautical University Worldwide assistant professor of aeronautics and program chair for the Master of Science in Unmanned Systems degree in the College of Aeronautics – investigates outreach and engagement efforts between airports and the unmanned aerial systems (UAS) operational communities. "Understanding the environments where this technology presents some of the most significant technological and safety challenges, while establishing a clear path to disseminate research findings and recommendations, will be essential to realizing success," he says. "By working with a well-rounded, connected and experientially diverse team at Embry-Riddle, we are able to bring unique perspectives and possible solutions to the table."

Google's self-driving car outfit shacked up with Intel to give its cars the brains they need to drive safely. The AI expert, who launched Google's self-driving car project in 2009, didn't explain the dog. He did announce that his online education company, Udacity, is launching a "nanodegree" program for wannabe flying car engineers. And he noted that since Udacity started its self-driving car engineer course a year ago, it has enrolled more than 10,000 students eager to help build the future of transportation.

Last year, my lab at Georgia Tech created Jill Watson, an A.I.-powered virtual teaching assistant designed to help answer students' questions in the discussion forum of an online class on artificial intelligence. To assess Jill's performance properly, we chose not to reveal her identity until the conclusion of the class. Mr. Etzioni characterized our experiment as an effort to "fool" students. The point of the experiment was to determine whether an A.I. agent could be indistinguishable from human teaching assistants on a limited task in a constrained environment. When we did tell the students about Jill, their response was uniformly positive.

If you need answers to one or more of these questions, you have come to the right place! Machine learning and neural networks are the hottest topic out there. Self driving cars, image recognition, ecommerce, predicting customer behavior, stock market predictions, you name it! Google, Facebook, Tesla, Amazon, Alibaba,... all great companies are working on this topic. Because of that we all should familiarize ourselves with this topic.

About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.