Before my eyes were even open, I spoke to my smart assistant this morning: "Alexa," I asked hopefully, "what time is it?" She kindly replied, "The time is seven twenty-seven a.m." I did not need her to tell me the morning was going to be a struggle. An hour later, dropping the kids off at school, I realized their violins were left in haste by the front door. As I made the U-turn in the school parking lot, I thought to myself, "Inconceivable! I have all this technology surrounding me! How long do I have to wait for this to get easier?"

Ideal for the aspiring developer, this collection boasts more than 30 hours of training on the technology that powers today's AI breakthroughs. Make your way through each of the four courses, and you'll foster new, practical knowledge in machine learning algorithms, AI applications, and more. Artificial Intelligence & Machine Learning Training -- This course offers a solid introduction to the current and potential applications of AI. You'll learn the basic ideas and techniques used in the design of intelligent computer systems, and, once you have your feet wet, you'll advance to statistical and decision-theoretic modeling paradigms, deep learning, and a host of other advanced topics. Introduction to Machine Learning -- In just two hours, this course will walk you through machine learning, the technology that powers self-driving cars, search engines, and more of today's AI breakthroughs.

ASK 100 students what they want from an MBA programme and you're likely to get 100 different answers. However, ask them what they want more of, and trends are easier to discern. At the Kellogg School of Management at Northwestern University, a survey of the current class earlier this year asked what students wanted to learn more about. "It has rapidly consumed a lot of mental real estate with our MBA students," says Brian Uzzi, who teaches a course on AI to MBAs at Kellogg. AI has become a key tool for businesses in all industries.

Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions. Artificial Neural Networks are models loosely based on how neural networks work in a living being.

With the emergence of big data and modern technologies, artificial intelligence has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for artificial intelligence in fields such as robotics, predictive analytics, finance, and many more. So, if you're a developer who wants to upgrade your normal applications to smart and intelligent versions, then go for this Learning Path. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

You will find here nine interesting topics that you won't learn in college classes. Most have interesting applications in business and elsewhere. They are not especially difficult, and I explain them in simple English. Yet they are not part of the traditional statistical curriculum, and even many experienced data scientists with a PhD degree have not heard about some of these concepts. This is a well known model, used as a base stochastic process to model the logarithm of stock prices, yet it has interesting properties (depending on dimension) that few people know about.

By Meg Murphy A person watching videos that show things opening -- a door, a book, curtains, a blooming flower, a yawning dog -- easily understands the same type of action is depicted in each clip. "Computer models fail miserably to identify these things. How do humans do it so effortlessly?" asks Dan Gutfreund, a principal investigator at the MIT-IBM Watson AI Laboratory and a staff member at IBM Research. "We process information as it happens in space and time. How can we teach computer models to do that?"

TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models..

TensorFlow is an open source software library for Machine Intelligence. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through video on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.This guide covers important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production.

Following in the footsteps of the University of Toronto and the University of Alberta, the University of Waterloo has launched an artificial intelligence (AI) research institute of its own. Officially opened on April 6, 2018, the Waterloo Artificial Intelligence Institute will have a broad mandate, including foundational research into statistical learning, deep learning, game theory, and data science; and operational research into secure, scalable solutions for a variety of industries including healthcare, environmental protection, urban planning, manufacturing, autonomous systems and human-machine interaction. Led by co-directors Fakhri Karray, a professor of electrical and computer engineering, and Peter van Beek, a professor of computer science, and sponsored by multiple high-profile companies including General Motors Canada, Huawei Technologies, Loblaw Companies Ltd., Shopify, and Magna International, the Institute aims to give researchers easy access to both experts and industry, whether for solutions development or what its founders called a "reverse co-op" program. "We would like the Waterloo AI Instititute to be the bridge between the partner companies and the University of Waterloo," Karray said in a video posted Friday. "They could channel to us some challenging problems, and faculty members, students, work on those problems, they cooperate with each other to solve these types of problems, and benefit the society at large."