RapidMiner, the company that delivers real data science, fast and simple, today announced another quarter of record revenue and new customer acquisition. Within the quarter, RapidMiner signed or expanded relationships with over 10% of the companies currently on the 2017 Fortune 100 Global List, and nearly 40% of Fortune 500 are active users of RapidMiner. "High value business use cases need data science and machine learning. Organizations looking for customer analytics, operational analytics, and risk analytics are coming to RapidMiner in record numbers," said Peter Lee, chief executive officer at RapidMiner. "As a result, we are seeing accelerated momentum in every part of our business."

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Deep multi-task learning with low level tasks supervised at lower layers.

Academia and industry at first glance appear to be strange bedfellows. One focuses on the theoretical and conceptual, whilst the other is driven by the practicalities of deadlines, goals and ultimately, profit. I have worked on AI in both of these roles, and throughout my 20 year career I've come to the realisation that, when it comes to driving innovation, these two distinct spheres need to work together. AI promises to be the most important technology of the future, and if the lofty ambitions and out-of-the-box thinking of academia can find synergy with the can-do attitude, urgency and resources of industry, we'll see an explosion in its applications. In fact, I believe that AI and ML technology won't just be a nice feature but will be a requirement for all applications go forward. This collaboration between industry and academia has been growing for some time and, like most things in technology, it all boils down to the data.

Are brands interacting with their customers in the right way to get useful feedback? The answer is: no, not always. Given that a recent Forbes interview with a survey expert indicates that response rates are down to an all-time low of less than 2%, brands could be forgiven for thinking their customers don't want to talk to them. However, the rise in the number of people using social media to contact brands would suggest otherwise. It is simply that they have survey fatigue, and the flaws in many survey programs are putting customers off from the start.

About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three.

About this course: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people's opinions and preferences, in addition to many other kinds of knowledge that we encode in text. This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern.

About this course: In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory.

This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service.

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).