With the machine learning market size expected to grow from $1.03 Billion USD in 2016 to $8.81 Billion USD by 2022, it can almost be said that machine learning is taking over the world. With that, there is a growing need for professionals who know the ins and out of machine learning. According to Forbes, machine learning patents grew at a 34 percent Compound Annual Growth Rate (CAGR) between 2013 and 2017, which is the third-fastest growing category of all patents granted. Also, the International Data Corporation (IDC) forecasts that spending on AI and ML will increase from $12 Billion USD in 2017 to $57.6 Billion USD by 2021. Even Deloitte Global predicts that the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.

Machine-learning engineer was the fastest growing job category in the five years to 2017, according to LinkedIn. But tech's hottest role isn't a simple field to break into, requiring at least high school math and some programming knowledge, even to get started. Luckily there are an increasing number of options for those wanting to get a grounding in the field, with Amazon Web Services (AWS) being the latest tech giant to release a set of machine-learning courses for free. That's in addition to the existing well-regarded material available online from the likes of fast.ai and Andrew Ng and Coursera. If you're interested in these courses, it's worth noting that you'll benefit more if you have a basic knowledge of Python and high school linear algebra, statistics, and calculus.

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The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

The seminar on Speech and Language Technology Tools is organized by HunCLARIN, Juhász Gyula Faculty of Pedagogy at the University of Szeged and the Hungarian Association of Applied Linguists and Language. The aim of the seminar is to provide researchers, teachers and students working in the humanities and social sciences a broad view of the corpora and state-of-the-art software in speech and language processing developed for Hungarian, mostly as part of the activities of HunCLARIN. This seminar is supported by CLARIN ERIC. The seminar took place at the Juhász Gyula Faculty of Education, University of Szeged, Hungary on Friday, 19 October, 2018.

HunCLARIN, Juhász Gyula Faculty of Pedagogy at the University of Szeged and the Hungarian Association of Applied Linguists and Language organized a Seminar on Speech and Language Technology Tools. The aim of the seminar is to provide a broad view of the corpora and state-of-the-art software in speech and language processing developed for Hungarian.

Admit it: At some point in your life, you've likely imagined how much easier it would be to have a personal robot or array of gadgets at your disposal--that is until you consider the hassle of going to school for an engineering degree. But now, thanks to the Arduino platform, virtually anyone can make their own robots, wearables, and more--no degree required. You can get started with the Complete Arduino Starter Kit & Course Bundle, on sale for only $72 today with code CMSAVE20. Arduino is an open-source prototyping platform that's used to create a wide range of electronics projects. Perfect for first timers, those who purchase this bundle will receive an Arduino hardware kit and access to six courses that'll show you how to use it.

I have completed Bachelor of Engineering (mechanical stream). I am currently working in the aerospace service industry and am eagerly looking forward to switching to Data Science. Two reasons behind this thought are: I am pretty good in mathematical and analytical skills, while Data Science is one of the remunerative jobs and this industry is expected to grow exponentially. What is your suggestion on this? Being a non-programming professional, can I learn Data Science?

Amazon announced today that it's making its range of machine learning courses available to all developers signed up to its AWS platform for free. This program was previously available only to Amazon employees, but anyone can now take advantage of it at no charge by signing up to Amazon Web Services' free plan. It includes 30 courses in total, with over 45 hours of course material, videos, and lab tests. The company says its content addresses beginners, advanced developers, and everyone in between. The company is targeting four groups with this program: developers, data scientists, data platform engineers, and business professionals.

This course, taught originally at UCL and recorded for online access, has two interleaved parts that converge towards the end of the course. One part is on machine learning with deep neural networks, the other part is about prediction and control using reinforcement learning. The two strands come together when we discuss deep reinforcement learning, where deep neural networks are trained as function approximators in a reinforcement learning setting. The deep learning stream of the course will cover a short introduction to neural networks and supervised learning with TensorFlow, followed by lectures on convolutional neural networks, recurrent neural networks, end-to-end and energy-based learning, optimization methods, unsupervised learning as well as attention and memory. Possible applications areas to be discussed include object recognition and natural language processing.