Today, the average company's workforce is not able to continuously refresh the knowledge and skill levels needed to capitalize on new business opportunities. This situation threatens to worsen over time. Here's a different approach: machine learning and artificial intelligence solutions can proactively offer the workforce an entirely new, future-oriented learning experience across devices and channels--one that is customized, personalized, dynamic and predictive. Business leaders know that thriving in the digital age requires them to take on the disruptive forces changing their industry--with speed, confidence and bold new bets. Nothing less than a similarly bold approach to "new skilling" will prepare the workforce to support continuous innovation and growth.

Young students can shape up their career orientation only when they are not loaded with content-based curriculum, CBSE's Skill Education Director Biswajit Saha said on Tuesday. The flexibility in the system should be adopted by the school curriculum and the focus needs to be on activity-based skill formation of students, he said at an education summit organised by the Associated Chambers of Commerce and Industry of India. As part of new-age skill education, CBSE has planned to offer Artificial Intelligence (AI) as an optional sixth subject for class 11 students from academic session 2109-20 onwards. Further, an AI-inspired module of 12 hours will be introduced for class 8 students by CBSE. Apart from artificial intelligence, subjects such as yoga, early childhood education will also be introduced as electives.

Young students can shape up their career orientation only when they are not loaded with content-based curriculum, CBSE's Skill Education Director Biswajit Saha said on Tuesday. The flexibility in the system should be adopted by the school curriculum and the focus needs to be on activity-based skill formation of students, he said at an education summit organised by the Associated Chambers of Commerce and Industry of India. As part of new-age skill education, CBSE has planned to offer Artificial Intelligence (AI) as an optional sixth subject for class 11 students from academic session 2109-20 onwards. Further, an AI-inspired module of 12 hours will be introduced for class 8 students by CBSE. Apart from artificial intelligence, subjects such as yoga, early childhood education will also be introduced as electives.

Machine learning engineers--i.e., advanced programmers who develop artificial intelligence (AI) machines and systems that can learn and apply knowledge--are in high demand, as more companies adopt these technologies. These professionals perform sophisticated programming, and work with complex data sets and algorithms to train intelligent systems. While many fear that AI will soon replace jobs, at this phase in the technology's development, it is still creating positions like machine learning engineers, as companies need highly-skilled workers to develop and maintain a wide range of applications. To help those interested in the field better understand how to break into a career in machine learning, we compiled the most important details and resources. This guide on how to become a machine learning engineer will be updated on a regular basis.

TDWI provides individuals and teams with a comprehensive portfolio of business and technical education and research to acquire the knowledge and skills they need, when and where they need them. The in-depth, best-practices-based information TDWI offers can be quickly applied to develop world-class talent across your organization's business and IT functions to enhance analytical, data-driven decision making and performance. TDWI advances the art and science of realizing business value from data by providing an objective forum where industry experts, solution providers, and practitioners can explore and enhance data competencies, practices, and technologies. TDWI offers conferences, summits, and seminars with both in-person and virtual attendance. Every month, you can find us teaching full-day courses on the hottest topics in data.

When materials science and engineering (MSE) specialists study substances at the molecular level, they are better able to alter their mechanical properties. Using electron microscopy and other techniques, they have been able to visualize single atoms and tailor materials to meet market demands. However, demand is growing at a rate that outpaces traditional MSE development tools. To address this demand, engineers can combine machine learning and materials science technologies to investigate how to optimize mechanical properties. As computers get faster and storage gets larger, the ability to collect and assess big data sets increases.

This Machine Learning Algorithms Tutorial video by Learnaholic India will help you learn Machine Learning Tutorial, what is Machine Learning, [Data Science] various Machine Learning problems and the algorithms, key Machine Learning algorithms with simple examples. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest and KNN algorithm. Machine Learning Tutorial [Data Science] Top 10 Machine Learning Algorithms for Beginners In this Machine Learning Algorithms Tutorial video you will understand: 1) Types of Machine Learning Algorithms (00:25) 2) Supervised Learning Algorithms (00:30) 3) Unsupervised Learning Algorithms (1:59) 4) Reinforcement Learning Algorithms (3:38) 5) Top 10 Machine Learning Algorithms for Beginners (4:33) This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a data-set for model creation and validation and how you can create a model using any machine learning algorithm! Hit the subscribe button above.

One out of four children in India are leaving grade eight without basic reading skills. Measuring the reading levels in a vast country like India poses significant hurdles. Recent advances in machine learning opens up the possibility of automating this task. However, the datasets are primarily in English. To solve this assessment problem and advance deep learning research in regional Indian languages, we present the ASER dataset of children in the age group of 6-14. The dataset consists of 5,300 subjects generating 81,658 labeled audio clips in Hindi, Marathi and English. These labels represent expert opinions on the ability of the child to read at a specified level. Using this dataset, we built a simple ASR-based classifier. Early results indicate that we can achieve a prediction accuracy of 86 percent for the English language. Considering the ASER survey spans half a million subjects, this dataset can grow to those scales.

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.

--With the rapid evolution of wireless mobile devices, it emerges stronger incentive to design proper collaboration mechanisms among the intelligent agents. Following their individual observations, multiple intelligent agents could cooperate and gradually approach the final collective objective through continuously learning from the environment. In that regard, independent reinforcement learning (IRL) is often deployed within the multi-agent collaboration to alleviate the dilemma of non-stationary learning environment. However, behavioral strategies of the intelligent agents in IRL could only be formulated upon their local individual observations of the global environment, and appropriate communication mechanisms must be introduced to reduce their behavioral localities. In this paper, we tackle the communication problem among the intelligent agents in IRL by jointly adopting two mechanisms with different scales. For the large scale, we introduce the stigmergy mechanism as an indirect communication bridge among the independent learning agents and carefully design a mathematical representation to indicate the impact of digital pheromone. For the small scale, we propose a conflict-avoidance mechanism between adjacent agents by implementing an additionally embedded neural network to provide more opportunities for participants with higher action priorities. Besides, we also present a federal training method to effectively optimize the neural networks within each agent in a decentralized manner . Finally, we establish a simulation scenario where a number of mobile agents in a certain area move automatically to form a specified target shape, and demonstrate the superiorities of our proposed methods through extensive simulations. I NTRODUCTION With the rapid development of mobile wireless communication and IoTs (Internet of Things) technologies, many scenarios gradually arise where the collaboration among the involved intelligent agents is highly required, such as the deployment of unmanned aerial vehicles (UA Vs) [1]-[3], the distributed control in the field of industry automation [4]-[6], and mobile crowd sensing and computing (MCSC) [7], [8]. In these scenarios, traditional centralized control methods are usually impracticable because of the restriction from limited computing resources as well as the demand for ultra-low latency and ultra-high reliability. As an alternative, multi-agent collaboration can be introduced into these scenarios to reduce the pressure at the central controller side. As one of the primary goals in the field of artificial intelligence (AI), assisting autonomous agents to act optimally through the "trial-and-error" interaction process with the expected environment is regarded as an important target of reinforcement learning (RL) [9]-[11].