It is aimed at providing industry exposure to students where experts from reputed organisations guided students to get a deeper understanding of artificial intelligence and machine learning techniques. Madhusudhan Govindaraju, Professor, Department of Computer Science and Vice Provost for International Education and Global Affairs (IEGA), Binghamton University, New York was the chief guest. He mentioned about opportunities available for MAHE students and faculty at Binghamton University. M. D. Venkatesh, Vice Chancellor, MAHE, also spoke at the event, a release mentioned.

This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL. Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis.

Data science offers all the opportunities of the hottest profession to the current tech-savvy generation, who are highly interested to work in data science than traditional engineering careers. Data analytics courses are in huge demand among the courses for data professionals. Students can access multiple online data science courses on multiple educational platforms having collaborations with reputed educational institutes. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates. Let's explore some of the top trending data science courses in 2022 to take up to become a competent data science professional.

The history of decision-making has always been intrinsically tied to the history of technology. Charts and compasses have guided explorers for centuries, and a level is an indispensable instrument for construction workers. New tools allow us to make more informed choices which, in turn, may positively impact technological advancements. This dependence suggests that a change in the technological landscape will have implications in how we make decisions. The last half-century has seen one of the most radical revolutions: the emergence of artificial intelligence (AI), powered by the ever-increasing data we gather.

It's quite a Canadian success story – (Ottawa) has decided that this is something we need to invest in,

There are over six million students enrolled in Machine Learning courses on Udemy. The most daring will try to start their own businesses. Here is a list of Udemy courses based on the 1Mby1M methodology that will assist budding entrepreneurs in creating a pragmatic strategy. I believe, strongly, that entrepreneurship and entrepreneurial capitalism can be democratized, and wealth can be created in the middle of the pyramid using capitalistic principles. In the next 2-3 decades, the potential for distributed capitalism is very high and the outcome should be extremely positive around the world.

In this popular beginner-level Specialization, you'll develop management, leadership, finance, and digital marketing skills that can translate to the successful operation of a business. Learn the basics of running a business and develop new strategies for improving an organization's growth and profitability by analyzing financial statements, creating forecasting and budgeting, and embracing digital marketing best practices.

Unless you already have a specific amount of training, you may be asking yourself, "What is MLOps?" In machine learning engineering, MLOps is a critical component focusing on optimizing the process of both deploying machine learning models and maintaining machine learning models. MLOps cannot be handled by a single position, but requires a team typically made up of data scientists, DevOps (development operations) engineers, and IT professionals. In this guide, we will break down what MLOps stands for, how it is used, and how to get started effectively using MLOps for your projects and goals. MLOps stands for machine learning operations.

Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning. The presence of large degrees of freedom (DoFs) available in these systems makes it difficult to balance the trade-off between learning the full dexterity and accomplishing manipulation goals. The motor learning literature argues that humans use motor synergies to reduce the dimension of control space. Using the low-dimensional space spanned by these synergies, we develop a computational model based on the internal model theory of motor control. We analyze the proposed model in terms of its convergence properties and fit it to the data collected from human experiments. We compare the performance of the fitted model to the experimental data and show that it captures human motor learning behavior well.

The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.