Udacity will be at the International Motor Show (IAA) in Frankfurt, Germany, this week! I'll be flying over on Wednesday as part of Lufthansa's FlyingLab which is a little bit like South by Southwest in the sky. My main event in Frankfurt will be at the me Convention, which is a conference put on by Mercedes-Benz in conjunction with SXSW. On Friday afternoon I'll be speaking on a panel entitled, "Teaching Machines to Drive Like Humans", with Sarah Marie Thornton from Stanford, and Danny Shapiro from NVIDIA. Late Friday afternoon, Udacity will be at the Speaker's Corner at the IAA New Mobility World.

What is Artificial Intelligence (AI)? Artificial Intelligence (AI) is the study of computer science focusing on developing software or machines that exhibit human intelligence. "AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future." Predictive systems– These AI are made to look at statistical data and form valuable conclusions. . Editing Software's– Here AIs suggest the ways that can be used to make pictures and texts more attractive. .

The data industry is a rapidly evolving space with the development of new technologies, methodologies, and platforms almost every other day. So, keeping pace with this industry can be challenging indeed. I have, therefore, created a list of 15 tech and data science influencers who are not just a source of inspiration to data science professionals and aspirants alike but also ensure that you keep abreast of all the new developments. I have not taken social media influence and related metrics into account. I have created this list based on how much I personally enjoy the content these individuals share on their social media.

Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.

Big Data is the future, and it's right here, right now! There's no doubt about it that Big Data is a powerful discovery tool, but all too often when you analyse a lot of data, you end up with a lot of results - too many, in fact, to be able to hold them all in your head simultaneously. So I'll amend my earlier statement: Data Visualisation is the future, and it's right here, right now! Apparently, visuals are processed 60,000 times faster in the brain than text, and are more easily committed to long-term memory. Visuals also make it easier to tell stories with data. Hey - I think I've heard that before somewhere...(see website footer for a clue!). Most of all though - visuals can help to simplify complex information.

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Dr. Kidd is focused on innovating within the rapidly changing healthcare technology market. He is the founder and CEO of Catalia Health, a company that delivers patient engagement across a variety of chronic conditions. You can find all the interviews here. We'll be posting them regularly on Robohub.

This course will explain how to use scikit-learn to do advanced machine learning. If you are aiming to work as a professional data scientist, you need to master scikit-learn! It is expected that you have some familiarity with statistics, and python programming. It's not necessary to be an expert, but you should be able to understand what is a Gaussian distribution, code loops and functions in Python, and know the basics of a maximum likelihood estimator. The course will be entirely focused on the python implementation, and the math behind it will be omitted as much as possible.

Over the years, Andrew Ng has worn many hats - Coursera co-founder, former Baidu chief scientist, founding lead of Google Brain team, and Stanford University adjunct professor. But lately, he has emerged as the leading influencer championing artificial intelligence (AI). Well over 1.5 million people have enrolled in his AI courses in Coursera. In a chat with Vinod Mahanta, Ng talks about recent AI controversies: Elon Musk versus Mark Zuckerberg spat on dangers of AI, Facebook AI chatbots creating their own language and job displacements. Edited excerpts: In an experiment recently, Facebook chatbots created their own language and had to be shut down.

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at causes of STEM attrition, let alone the use of machine learning to examine how well this phenomenon can be predicted. In this paper, we detail our efforts to model and predict dropout from STEM fields using one of the largest known datasets used for research on students at a traditional campus setting. Our results suggest that attrition from STEM fields can be accurately predicted with data that is routinely collected at universities using only information on students' first academic year. We also propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. We believe these results show great promise in using machine learning to improve STEM retention in traditional and non-traditional campus settings.