American Institute of A.I. offers 12 executive courses and 7 certification programs. Click the courses below to read the course descriptions.

This course created by Data Weekends, Jose Portilla, and Francesco Mosconi is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

Google has taught its DeepMind AI to navigate a parkour course by using reinforcement learning. Reinforcement learning is the practice of rewarding desirable behaviour. The faster the AI could navigate the virtual parkour course, the greater the reward. Further incentives and penalties were added for various other metrics. "We train several simulated bodies on a diverse set of challenging terrains and obstacles, using a simple reward function based on forward progress," explains Nicolas Heess, a researcher on the project.

This three-day course is for data engineers, analysts, architects; software engineers; IT operations; and technical managers interested in a thorough, hands-on overview of the Apache Spark platform. The course covers the core APIs for using Spark, fundamental mechanisms and basic internals of the platform, SQL and other high-level data access tools, as well as Spark's streaming capabilities and machine learning APIs. Each topic includes slide and lecture content along with hands-on use of Spark through the elegant Databricks web-based notebook environment. Inspired by tools like IPython/Jupyter and Matlab, Databricks notebooks allow attendees to code jobs, data analysis queries, and generate visualizations using their own cloud-based Spark cluster, accessed through a web browser.* Duration: 3 Days, Full Time (9AM to 5PM) We will have a break from noon to 1pm daily; lunch will not be provided, but there are several options nearby.

This training is ideal for engineers creating algorithms and software for visual machine perception in all types of applications (e.g. in the industrial, medical, consumer, retail, public safety or automotive area) who want to quickly come up to speed on using TensorFlow for these applications. It's also appropriate for managers who want to get a flavor for creating deep neural networks and using TensorFlow. After the training, attendees will be ready to begin using TensorFlow productively in their work. In addition, for attendees who require an introduction to deep neural network algorithms, the Embedded Vision Alliance will offer a two-hour video tutorial presentation online prior to the TensorFlow class.

For decades, textbooks were seen as the foundation for instruction in American schools. These discipline-specific tomes were a fundamental part of the educational infrastructure, assigned to students for each subject and carried in heavy backpacks every day – from home to school and back again. The experience of students is much different today. As a scholar of learning technologies and a director for outreach and engagement at Ohio State's College of Education and Human Ecology, we've seen how technological advances and an increase in digital curriculum materials have hastened the move away from textbooks. Does all of this technology spell the end of traditional textbooks?

The Second Edition of the award-winning Springer Handbook of Robotics edited by Bruno Siciliano and Oussama Khatib has recently been published. The contents of the first edition have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Most previous chapters have been revised, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook's team. Like for the first edition, a truly interdisciplinary approach has been pursued in line with the expansion of robotics across the boundaries with related disciplines. Again, the authors have been asked to step outside of their comfort zone, as the Editorial Board have teamed up authors who never worked together before.

Since we will be using scientific computing and machine learning packages at some point, I suggest that you install Anaconda. This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Gaining an intimate understanding of machine learning algorithms is beyond the scope of this article, and generally requires substantial amounts of time investment in a more academic setting, or via intense self-study at the very least. For example, when you come across an exercise implementing a regression model below, read the appropriate regression section of Ng's notes and/or view Mitchell's regression videos at that time.

Chatbots, deep learning, concierge apps, big data -- these words may seem meaningless today, but they offer an incredible opportunity available for event professionals who are willing to embrace innovation. Artificial intelligence (AI) will soon revolutionize the events industry and play a pivotal role in growing your event. From customer support to event management and marketing, AI may change how we experience live events -- and give yours a competitive advantage. Join Event Manager Blog's Julius Solaris as he answers your most burning questions about AI. What is useful and what is not?

Recently, one of my friends and I were solving a practice problem. After 8 hours of hard work & coding, my friend Shubham got a score of 1153 (position 219). How did I get there? What if I tell you there exists a library called MLBox, which does most of the heavy lifting in machine learning for you in minimal lines of code? From missing value imputation to feature engineering using state-of-the-art Entity Embeddings for categorical features, MLBox has it all.