Curriculum



How AI could help you learn sign language

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Sign languages aren't easy to learn and are even harder to teach. They use not just hand gestures but also mouthings, facial expressions and body posture to communicate meaning. This complexity means professional teaching programmes are still rare and often expensive. But this could all change soon, with a little help from artificial intelligence (AI). My colleagues and I are working on software for teaching yourself sign languages in an automated, intuitive way.


AI Technology is Disrupting the Traditional Classroom. Here's a Progress Report.

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"You've got a perfect storm, really," says Rose Luckin, a professor at University College London who has studied AIEd for the past 20 years. "You can do things that you weren't able to do before." AIEd now helps investigate the steps students go through when learning subjects from calculus to chemistry, shining a light on what individual learners need to progress. To get there, an AI program is first trained on hundreds or thousands of students' work, gaining a knowledge base of the common areas that give learners trouble. Then over time, as an individual uses the system, the AI homes in on specifics to focus on, usually offering bespoke lessons to brush up on skills, and, in some cases, offer pep talks through bots.


Reviewing 2018 and Previewing 2019

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Kaggle Kernels is our hosted data science environment. It allows our users to author, execute, and share code written in Python and R. Kaggle Kernels entered 2018 as a data science scratchpad. In 2018, we added key pieces of functionality that make it a powerful environment. This includes the ability to use a GPU backend and collaborate with other users. We had 346K users author kernels in 2018, up 3.1x from 111K in 2017.


Simply deep learning: an effortless introduction – Towards Data Science

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This article is part of the Intro to Deep Learning: Neural Networks for Novices, Newbies, and Neophytes Series. Let's start with a quick recap from part 1 for anyone who hasn't looked at it: At a very basic level, deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for deep learning is the way that the human brain filters information. Its purpose is to mimic how the human brain works to create some real magic. Deep learning attempts to mimic the activity in layers of neurons in the neocortex.


How AI Could Help You Learn Sign Language

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Sign languages aren't easy to learn and are even harder to teach. They use not just hand gestures but also mouthings, facial expressions and body posture to communicate meaning. This complexity means professional teaching programmes are still rare and often expensive. But this could all change soon, with a little help from artificial intelligence (AI). My colleagues and I are working on software for teaching yourself sign languages in an automated, intuitive way.


Data Science and Machine Learning – MITU Skillologies

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Python Type Conversion, Decisions and Loops – Type Conversion – Decision making -- a) If.. -- b) If..else -- c) Nested if..else -- d) If..elif..else – Loops -- a) For loop -- b) While loop -- c) Nested loops -- d) break, pass and continue


Using artificial intelligence to assess clinicians' communication skills

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AI research involves the development of "intelligent" computer agents. Traditionally, AI encoded existing knowledge about the world and thereby relied on prespecified human expertise. The hard coding of information into AI algorithms was typically a lengthy process. An alternative approach, known as machine learning, relies less on prior assumptions and enables computers to develop algorithms based on repeated trials and errors. Although this still requires expert human input, the time to develop AI algorithms is now often much shorter.


Introducing Feast: an open source feature store for machine learning Google Cloud Blog

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To operate machine learning systems at scale, teams need to have access to a wealth of feature data to both train their models, as well as to serve them in production. GO-JEK and Google Cloud are pleased to announce the release of Feast, an open source feature store that allows teams to manage, store, and discover features for use in machine learning projects. Developed jointly by GO-JEK and Google Cloud, Feast aims to solve a set of common challenges facing machine learning engineering teams by becoming an open, extensible, unified platform for feature storage. It gives teams the ability to define and publish features to this unified store, which in turn facilitates discovery and feature reuse across machine learning projects. "Feast is an essential component in building end-to-end machine learning systems at GO-JEK," says Peter Richens, Senior Data Scientist at GO-JEK, "we are very excited to release it to the open source community. We worked closely with Google Cloud in the design and development of the product, and this has yielded a robust system for the management of machine learning features, all the way from idea to production."


Machine Learning: End-to-End guide for Java developers – The world's largest ebook library

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This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. This course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization.