Every face does not tell a story; it tells thousands of them. Over evolutionary time, the human brain has become an exceptional reader of the human face--computerlike, we like to think. A viewer instinctively knows the difference between a real smile and a fake one. In July, a Canadian study reported that college students can reliably tell if people are richer or poorer than average simply by looking at their expressionless faces. Scotland Yard employs a team of "super-recognizers" who can, from a pixelated photo, identify a suspect they may have seen briefly years earlier or come across in a mug shot.

When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. We'll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions that can help when you encounter such scenarios. If we were to train a machine learning model and it learned to always predict an email as not spam (negative class), then it would be accurate 99% of the time despite never catching the positive class. Similarly, increasing the number of training examples can help in cases of high variance, helping the machine learning algorithm build a more generalizable model.

So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. If you already have some background, or don't need all the components, feel free to adapt your own paths and let us know how you made changes in the path.

As artificial intelligence creeps deeper into each and every industry vertical, demand for experienced technical talent only continues to increase. Now online education tools like Udacity and Coursera are being thrust into the spotlight as potential solutions to the problem. But even as course enrollment in AI classes balloons, Fortune 2000 companies are still paying an unsustainable premium for data scientists. We're excited to showcase this ecosystem at Disrupt SF. Both Udacity's Sebastian Thrun and Coursera's Andrew Ng understand the potential repercussions of this trend intimately.

Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu's research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition.

You will be at an inherent disadvantage compared to master's/PhD students, but having a resume full of ML projects/research/internships is actually a lot better than simply more education. Big companies such as Facebook/Google will hire for general software engineering intern positions, but have a plethora of ML projects available, provided that you have demonstrable experience/background. You will be at an inherent disadvantage compared to master's/PhD students, but having a resume full of ML projects/research/internships is actually a lot better than simply more education. Big companies such as Facebook/Google will hire for general software engineering intern positions, but have a plethora of ML projects available, provided that you have demonstrable experience/background.

As artificial intelligence creeps deeper into each and every industry vertical, demand for experienced technical talent only continues to increase. Now online education tools like Udacity and Coursera are being thrust into the spotlight as potential solutions to the problem. But even as course enrollment in AI classes balloons, Fortune 2000 companies are still paying an unsustainable premium for data scientists. We're excited to showcase this ecosystem at Disrupt SF. Both Udacity's Sebastian Thrun and Coursera's Andrew Ng understand the potential repercussions of this trend intimately.

The Penn State EdTech Network will host Penn State faculty and researchers alongside IBM, Google, IPSoft and 14 other industry partners during EdTech Engage, Oct. 5-6, at several locations on the Penn State University Park campus. The symposium will create an environment where participants can examine how artificial intelligence and machine learning can improve the student experience and address operational challenges in a university setting. Larry Ragan, principal community aggregator for the Penn State EdTech Network, said people look at AI with skepticism and wonder. "EdTech Engage will provide attendees with a range of experiences to learn and explore existing AI technologies, a glimpse into future direction, and discussions built around the positive and negative ramifications of AI and machine learning in higher education," Ragan said. Catherine Solazzo, vice president of developer engagement for IBM Digital Business Group and keynote speaker at EdTech Engage, said artificial intelligence platforms like IBM Watson could transform the learning experience for both students and administrators.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Abdelrahman is a Software Development Engineer in the Core Machine Learning team at Amazon Robotics. His work includes bringing state-of-the-art machine learning techniques to tackle various problems for robots at Amazon's robotic fulfillment centers. You can find all the interviews here. We'll be posting them regularly on Robohub.

Msc in Data Sciences and Business Analytics is a brand new program proposed and coordinated by CentraleSupélec and ESSEC Business School . Check all details here: http://bit.ly/1JnOF5H Here is the Machine Learning course (First Lecture) by Prof. Chloé-Agathe Azencott, Centre for Computational Biology, Mines ParisTech. September 2015Part 1/7: What is machine learning? Machine learning lies at the heart of data science.