This is the next post of a series of Trends & Perspectives blog posts. The Track Chairs will reflect on each of this years' presentation tracks, analyze and discuss some of the trends that you can expect to hear about at OLC Accelerate this year, and also get the perspectives of the Best-in-Track winners. This blog post features the presenters for the Best In Track selection for the Teaching and Learning Effectiveness Track at the upcoming OLC Accelerate Conference. Their express workshop, Show it Off! Showcase Your Artificial Intelligence, will be held on Wednesday, November 20th from 1:15-2:00pm in Southern Hemisphere 1.

Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. The Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity for you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2019, then you have come to the right place.

Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world.

Data Science Specialization is one of the best known sets of courses offered by Coursera in conjunction with Johns Hopkins University. This specialization covers the concepts and tools you'll need throughout the entire data science pipeline. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses. Coursera John Hopkins Data Science is a ten course program that covers the data science process from data collection to the production of data science products. It focuses on implementing the data science process in R. Coursera Johns Hopkins data science certification includes 9 courses and a capstone project.

Deep Learning Specialization provides introduction to DL methods for computer vision applications for practitioners who are familiar with the basics of DL. You will discover a breakdown and review of the convolutional neural networks course taught by Andrew Ng on deep learning specialization. It does not focus too much on math and does not include any code. After finishing the specialization you will know how to build models for photo classification, object detection, face recognition, and more. Instructors patiently explain the requisite math and programming concepts in a carefully planned order for learners who could be rusty in math/coding.

NASA's mission of exploration requires leveraging new ways to utilize and learn from the unprecedented amount of data that space-based observation platforms generate. New capabilities are needed, ranging from onboard autonomy for robotic spacecraft to techniques for understanding the world and universe where we live. Artificial Intelligence (AI) and data science are rapidly becoming integral to NASA's future to drive automation and interpretation. AI is a collection of advanced technologies that allow machines to think and act, through sensing, comprehending, interacting, and learning. AI's foundations lie at the intersection of several traditional fields - Philosophy, Mathematics, Economics, Neuroscience, Psychology, and Computer Science.

Description: A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

From IBM's Watson to a McDonald's drive through, artificial intelligence is all around us but most business owners have yet to take advantage. Have you given thought to how best to apply AI to get an edge with your business? This webinar will focus on all the surprising ways AI can transform your business and keep things running smoothly 24/7.

First, we will freeze the base layers so that the layers are not trainable.

Last year marked the 50th anniversary of Stanley Kubrick's space-race era epic, "2001: A Space Odyssey", prompting several outlets to do side-by-side comparisons on how the film's depiction of digital technologies matched up against what we really had around the turn of the century. Of course, no one can do this without mentioning the film's central antagonist, HAL 9000, a sentient computer who interacts with the doomed characters through a verbal interface, reminiscent of the way that contemporary users deploy Amazon's Alexa to order pizza. So, while some might point to this similarity as proof of Kubrick's intuition, the reality is that we have been pushing towards zero U/I, or interfaces that do not rely on screens, for decades. The concept of zero U/I was first defined by then Fjord Design Director, now frog Creative Director, Andy Goodman, during a 2015 speech at San Francisco's Solid Conference. He describes it as a natural approach to user-interface interactions, which abandons the abruptness of the screen in favor of a more natural environment, through which users can communicate with devices using speech, motion, and even thought.