TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. This video will help you leverage the power of TensorFlow to perform advanced image processing. This course is a continuation of the Intro to Computer Vision course, building on top of the skills learned in that course. In this course, you'll dive deeper as we cover more advanced computer vision concepts. You will implement multiple state-of-the-art deep learning papers from scratch using the TensorFlow-Keras API.

This volume introduces working with text, with a focus on the most plentiful source of text out there: email. Working with email text from your own Gmail account, you will build up a label predictor, similar in effect to the technology Google uses to power the Social and Promotions tabs. With this technique, you will be able to build your own email classification and automated workflow hooks. Will Ballard serves as Chief Technology Officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the Executive Vice President of Technology and Engineering at Demand Media.

Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional'in-demand' analytical job skills to offer a prospective employer.

Microsoft is the latest company to offer courses in artificial intelligence (A.I.). Microsoft's Professional Program for Artificial Intelligence includes 10 online courses that teach 10 skills. Each course takes roughly 8-16 hours to complete and kicks off at the beginning of each quarter. The courses, which can be taken in any order, range from "Introduction to AI" to "Essential Mathematics for Artificial Intelligence." The artificial-intelligence program joins a number of other Microsoft professional tracks, including software development, IT support, DevOps, and more.

About this course: The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Although they are well known in the cognitive science and AI communities only moderate progress has been made towards building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing complex visual concepts based on this vocabulary. Using this language and Bayesian inference, complex visual concepts can be induced from the examples that are provided in each Bongard problem. Contrary to other concept learning problems the examples from which concepts are induced are not random in Bongard problems, instead they are carefully chosen to communicate the concept, hence requiring pragmatic reasoning. Taking pragmatic reasoning into account we find good agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself. While this approach is far from solving all Bongard problems, it solves the biggest fraction yet.

Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations.

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.