What if you could build a character that could learn while it played? Think about the types of game play you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves. In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. This is a valid question and the answer is simple.

Artificial intelligence (AI) is proving to be the most significant technological advancement across all industries in recent decades. While we're still years away from the robotics side of AI, the machine learning (ML) sector has exploded by helping companies with everything from improving customer retention rates to driving enhanced insights from big data and even mitigating supply chain risks. With the global machine learning market anticipated to grow from $1.4B in 2017 to $8.8B by 2022 according to a recent report by Research and Markets, here's a look at where those investments are headed and what it means for increasingly in-demand machine learning talent. Last year, Amazon introduced us all to Alexa in the workplace, but this voice-activated, AI-powered device is only the beginning. Natural language processing (NLP), made possible through machine learning, helps computers, systems, and solutions better understand the context and meaning of sentences.

Tensorflow, developed by Google, has become the most popular framework for deep learning, and now operates on a variety of devices including multicore CPUs, general purpose GPUs, mobile devices, and custom ASICs. In this on-demand webinar hosted by Intel and ActiveState, you'll get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why you should be running on a CPU.

The event will be an Introduction to Machine Learning .Net next Saturday 18th of August. There are several new features and improvements on the platform since the previous event, so it's time for me to review and prepare new content. Machine Learning has moved out of the lab and into production systems. Understanding how to work with this technology is one of the essential skills for developers today. In this session, you will learn the basics of machine learning, how to use existing models and services in your apps, and how to get started with creating your own simple models.

Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI.

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.

This course is all about A/B testing. A/B testing is used everywhere. A/B testing is all about comparing things. If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

Data scientists, artificial intelligence experts and machine learning developers are in hot demand right now -- so much, that these are some of the hardest jobs to fill. According to this year's best jobs report from Glassdoor, data scientist was the best job in the U.S. It was also the top job in 2017, and in 2016, up from ninth place in 2015. The number of job openings on the site rose from 3,449 in 2015 to 4,524 this year. And IBM predicts that the number of openings for U.S. data scientists and similar advanced analytical roles will reach 61,799 by 2020, with a 93 percent predicted growth rate in data science skills, followed by machine learning with 56 percent predicted growth. Get the latest insights with our CIO Daily newsletter.

Data scientists, artificial intelligence experts and machine learning developers are in hot demand right now -- so much, that these are some of the hardest jobs to fill. According to this year's best jobs report from Glassdoor, data scientist was the best job in the U.S. It was also the top job in 2017, and in 2016, up from ninth place in 2015. The number of job openings on the site rose from 3,449 in 2015 to 4,524 this year. And IBM predicts that the number of openings for U.S. data scientists and similar advanced analytical roles will reach 61,799 by 2020, with a 93 percent predicted growth rate in data science skills, followed by machine learning with 56 percent predicted growth. Get the latest insights with our CIO Daily newsletter.