Why you should listen Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2015 and 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan. The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks.

Finally, a comprehensive hands-on machine learning course with specific focus on regression based models for the investment community and any passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices.

Hoy traemos a este espacio a Make Music and Art Using Machine Learning, que nos presentan así; About Magenta Magenta is a Google Brain project to ask and answer the questions, "Can we use machine learning to create compelling art and music? Our work is done in TensorFlow, and we regularly release our models and tools in open source. These are accompanied by demos, tutorial blog postings and technical papers. To follow our progress, watch our GitHub and join our discussion group. It's first a research project to advance the state-of-the art in music, video, image and text generation. So much has been done with machine learning to understand content--for example speech recognition and translation; in this project we explore content generation and creativity. Second, Magenta is building a community of artists, coders, and machine learning researchers. To facilitate that, the core Magenta team is building open-source infrastructure around TensorFlow for making art and music. This already includes tools for working with data formats like MIDI, and is expanding to platforms that help artists connect with machine learning models Douglas Eck Education Innovation Human-Computer Interaction and Visualization Information Retrieval and the Web Machine Intelligence Natural Language Processing Co-Authors I'm a research scientist working on Magenta, an effort to generate music, video, images and text using machine intelligence. Magenta is part of the Google Brain team and is using TensorFlow (www.tensorflow.org), The question Magenta asks is, "Can machines make music and art?

Recently I wrote about the growing importance of investing in the skills of employees so that they can adapt to the changing technology landscape they're working in. It was based upon a recent Accenture report, which argued that organizations need to take a systemic approach to ensuring the interaction between man and machine is a smooth one. Whilst generally corporate training budgets are on a downward trend, there are a couple of recent developments that suggest all is not entirely lost and small progress is being made. The first comes from Google, who have teamed up with MOOC pioneer Coursera to launch an online course for IT support professionals. It's estimated that IT support roles will grow by 10% by 2026, and the course is designed to learn the kind of skills required to land such a role.

"This course has taught me many things I wanted to know about pandas. It covers everything since the installation steps, so it is very good for anyone willing to learn about data analysis in python /jupyter environment." "Good explanation, I have laready used two online tutorials on data -science and this one is more step by step, but it is good" "i have studied python from other sources as well but here i found it more basic and easy to grab especially for the beginners. I can say its best course till now . The average data scientist today earns $130,000 a year by glassdoor.

Ng's the co-founder of online education platform Coursera who's led AI teams at Google and Baidu. He says his background will help prioritize projects with the most potential, saving founders six months of development time otherwise lost explaining their idea. He's also tapping his personal network for leads: The fund, simply called AI Fund, isn't taking pitches from entrepreneurs.

About this course: Leaders in business and non-profit organizations increasingly work across national borders and in multi-cultural environments. You may work regularly with customers or suppliers abroad, or be part of a globally dispersed cross-functional team, or an expatriate manager on an international assignment. You may be a member of a global online community, or a development aid worker collaborating with an international network of partner organizations. In all of these contexts, your effectiveness as a leader depends on how well you understand and are able to manage individual and collective behaviors in an intercultural context. In this course – together with a team of Bocconi expert faculty and Bocconi alumni – we'll explore the theory and practice of international and intercultural leadership and organizational behavior.

Last Thanksgiving, I watched my father-in-law evaluate over one hundred exams for the high school class he teaches on the U.S. government. They were mostly short answer questions: matching different provisions of the U.S. Constitution, and explaining the contents of the Bill of Rights. The grading was tedious and time consuming, and took him hour after hour during what should have been a holiday. I started to wonder whether there could be a faster way. Automatic computer grading could do exactly that, learning from previous answers and getting better as it goes -- and it is already being used in some universities and for large online courses (MOOCs).

MACHINE LEARNING FOR APPS Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech. Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models.

This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries. After that we introduce the necessary skills for profiling your R code.