A high-level course of AI to learn how Machine Learning provides the foundation for AI, and how you can leverage cognitive services in your apps. Artificial Intelligence will define the next generation of software solutions. This computer science course provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people's lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI. Discover how machine learning can be used to build predictive models for AI.

This course is designed to make you an expert in AWS Machine Learning and it teaches you how to convert your cool ideas into highly scalable products in a matter of days. Biggest challenge for a Data Science professional is how to convert the proof-of-concept models into actual products that your customers can use. There are several courses on machine learning that teach you how to build models in R, Python, Matlab and so forth. However, converting a model into a scalable solution and integrating with your existing application requires a lot of effort and development. The real success of your ideas and concepts depends on how soon you can put the capabilities in the hands of your customers.

Today, every company is a technology company, with an ever shorter go-to-market cycle. Every professional role that is now touched by technology will soon be collaborating with an Artificial Intelligence (AI) system or – as we prefer – an'Augmented Intelligence' system. It will be the people, not only the technology, that will drive widespread AI adoption, so it's essential to democratise AI capability for the benefit of all roles, from marketing and legal to HR and operations. A new report on New Zealand's AI future highlights the critical importance of developers in particular. Artificial Intelligence: Shaping a Future New Zealand is an in-depth study by the AI Forum exploring the opportunities and impacts of AI in New Zealand.

New Zealander Dr Shane Legg is now chief scientist for Google DeepMind - an artificial intelligence program that aims to solve any complex problem without needing to be taught how. A leader in artificial intelligence first honed his skills at the University of Waikato. Now, after launching a computer program with the ability to learn on its own, he has returned to accept a Distinguished Alumni Award. Dr Shane Legg arrives at the Hamilton campus on Tuesday, and will trace the footsteps he first walked in 1993. He graduated in 1996, when the internet was a relatively new mechanism, and soon after went on to co-found Google DeepMind.

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.

Machine Learning is one of the most sought after skills these days. If you are a data scientist, then you need to be good at Machine Learning – no two ways about it. As part of DataFest 2017, we organized various skill tests so that data scientists can assess themselves on these critical skills. These tests included Machine Learning, Deep Learning, Time Series problems and Probability. This article will lay out the solutions to the machine learning skill test. If you missed out on any of the above skill tests, you can still check out the questions and answers through the articles linked above. In Machine Learning skill test, more than 1350 people registered for the test.

Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task givent that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement learning in multimodal machine translation. We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We experiment our proposed algorithm on the Multi30K multilingual English-German image description dataset and the Flickr30K image entity dataset. Our model takes two channels of inputs, image and text, uses translation evaluation metrics as training rewards, and achieves better results than supervised learning MLE baseline models. Furthermore, we discuss the prospects and limitations of using reinforcement learning for machine translation. Our experiment results suggest a promising reinforcement learning solution to the general task of multimodal sequence to sequence learning.

We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of $5$ feedback requests for every input.

The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself. The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks. With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You'll pick up all the core concepts that veteran Data Scientists understand intimately.

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!