Amazon Personalize is a machine learning service which enables you to personalize your website, app, ads, emails, and more, with custom machine learning models which can be created in Amazon Personalize, with no prior machine learning experience. AWS is pleased to announce that Amazon Personalize now supports ten times more item attributes for modeling in Personalize. Previously, you could use up to five item attributes while building an ML model in Amazon Personalize. This limit is now 50 attributes. You can now use more information about your items, for example, category, brand, price, duration, size, author, year of release etc., to increase the relevance of recommendations.

Artificial intelligence--code that learns--is likely to be humankind's most important invention. It's a 60-year-old idea that took off five years ago when fast chips enabled massive computing and sensors, cameras, and robots fed data-hungry algorithms. We're a couple of years into a new age where machine learning (a functional subset of AI), big data and enabling technologies are transforming every sector. In every sector, there is a big data set behind every question. Every field is computational: healthcare, manufacturing, law, finance and accounting, retail, and real estate.

You can read our students' testimonials for a better idea of Ubiqum's results. Everything we do is to help you start your professional life as a Data Analyst, Web Developer, or Mobile Developer. If you put in your best effort, we will do the rest. We are confident in our methods and content, demonstrated in the fact that we offer an employment guarantee which allows students to pay only half their tuition at the beginning of the course and the rest only once they've successfully secured employment.

Prudential Singapore is offering a free machine learning (ML) course for its 1,200 employees in Singapore, according to a report on Singapore broadsheet The Straits Times. Called "Machine Learning for Humans", the 30-hour course is developed together with Ngee Ann Polytechnic. With training by data scientists from both the public and private sectors, the online course offers assessments are offered for participants to apply what they have learned. It is understood that more than 170 employees have signed up so far. A foundational course called "AI in Finance" offered last year was taken by over 200 employees from Prudential.

With the aim to promote innovations in the edtech space using artificial intelligence, Noida-based edtech startup Extramarks has partnered with Indraprastha Institute of Information Technology Delhi (IIIT-Delhi). In this partnership, Extramarks will help IIIT-Delhi to set up an AI-based research laboratory in the computer science department to the institute. The laboratory will work to find new use cases of AI in the edtech segment. Explaining further how the partnership will work, the director of IIIT-Delhi Ranjan Bose said that with the help of Extramarks, the students will gain practical experience for their constructive research in the field of AI. Ritvik Kulshrestha, CEO of Extramarks Education, said that the company has already implemented AI in the schools where it is already offering digital learning programmes.

The issue of diversity in STEM fields is something which has been spoken about for some time, albeit until recently on a smaller scale. Is the issue of diversity, starting to be recognised at a greater scale in STEM fields? Has there been a shift in the increase in vocalising the discussion and creation of movements and organisations with the sole focus of addressing the concerns of those directly affected. Although increasingly spoken about, what are we doing to address diversity and how can we practically address the issue currently present? We asked some of our friends in STEM for their practical solutions.

The future of artificial intelligence (AI) is here: self-driving cars, grocery-delivering drones and voice assistants like Alexa that control more and more of our lives, from the locks on our front doors to the temperatures of our homes. For example, should an autonomous vehicle swerve into a pedestrian or stay its course when facing a collision? These questions plague technology companies as they develop AI at a clip outpacing government regulation, and have led Seattle University to develop a new ethics course for the public. Launched last week, the free, online course for businesses is the first step in a Microsoft-funded initiative to merge ethics and technology education at the Jesuit university. Seattle U senior business-school instructor Nathan Colaner hopes the new course will become a well-known resource for businesses "as they realize that [AI] is changing things," he said.

Link: Artificial Intelligence AI - Simply Explained for Beginners Coupon code / udemy Fundamentals of agent and multi-agent systems, neural networks, deep learning, machine learning & computer vision New by Axel Mammitzsch What you'll learn You will learn to understand the structure and design of modern artificial intelligence systems. You will learn to distinguish between strong and weak AI. You will learn what "Deep Learning" is. You will learn what "Deep Learning" is. What is the structure of a problem.

Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classification. But, efficient policy evaluation need not yield efficient estimation of policy parameters. We consider the estimation problem given by a weighted surrogate-loss classification reduction of policy learning with any score function, either direct, inverse-propensity weighted, or doubly robust. We show that, under a correct specification assumption, the weighted classification formulation need not be efficient for policy parameters. We draw a contrast to actual (possibly weighted) binary classification, where correct specification implies a parametric model, while for policy learning it only implies a semiparametric model. In light of this, we instead propose an estimation approach based on generalized method of moments, which is efficient for the policy parameters. We propose a particular method based on recent developments on solving moment problems using neural networks and demonstrate the efficiency and regret benefits of this method empirically.