Exploring meaningful structural regularities embedded in networks is a key to understanding and analyzing the structure and function of a network. The node-attribute information can help improve such understanding and analysis. However, most of the existing methods focus on detecting traditional communities, i.e., groupings of nodes with dense internal connections and sparse external ones. In this paper, based on the connectivity behavior of nodes and homogeneity of attributes, we propose a principle model (named GNAN), which can generate both topology information and attribute information. The new model can detect not only community structure, but also a range of other types of structure in networks, such as bipartite structure, core-periphery structure, and their mixture structure, which are collectively referred to as generalized structure. The proposed model that combines topological information and node-attribute information can detect communities more accurately than the model that only uses topology information. The dependency between attributes and communities can be automatically learned by our model and thus we can ignore the attributes that do not contain useful information. The model parameters are inferred by using the expectation-maximization algorithm. And a case study is provided to show the ability of our model in the semantic interpretability of communities. Experiments on both synthetic and real-world networks show that the new model is competitive with other state-of-the-art models.

At each step, get practical experience by applying your skills to code exercises and projects. This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.

In today's engineering curriculum, topics on probability and statistics play a major role, as the statistical methods are very helpful in analyzing the data and interpreting the results. When an aspiring engineering student takes up a project or research work, statistical methods become very handy. Hence, the use of a well-structured course on probability and statistics in the curriculum will help students understand the concept in depth, in addition to preparing for examinations such as for regular courses or entry-level exams for postgraduate courses. In order to cater the needs of the engineering students, content of this course, are well designed. In this course, all the sections are well organized and presented in an order as the contents progress from basics to higher level of statistics.

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

A new artificial intelligence graduate degree program at Iowa State University will be the first of its kind in the state. The Iowa Board of Regents approved the two-year master's of science degree program Thursday through consent agenda after being presented with the program Wednesday in committee. The graduate program is expected to begin this fall. Hridesh Rajan, a professor and chairperson of ISU's Department of Computer Science, said the new program seeks to produce graduates that can work on building and enhancing components of artificial intelligence -- not only to be able to understand and make practical use of machine learning and big data, but also be able to communicate the capabilities and limitations of AI. Artificial intelligence, or AI, is the study of techniques that help incorporate intelligence into software, Rajan said.

When it comes to Machine learning (ML), Python language becomes necessary to understand. Python is the coding language, which allows users to code and program a machine. It's simple syntax and programming language make it easy for the learners to code and develop a machine. Python is a very significant language for Machine Learning aspirants. Machine learning is incomplete without Python language.

Regulators have opened an investigation into Facebook amid concerns it is using its vast troves of personal data to push its own shopping and data tools. The probe by the UK's competition regulator will examine whether it is abusing its dominant position in online advertising. It comes amid growing antitrust concerns about the way many technology companies – not just Facebook but others such as Apple – have been able to use their vast size and hold on the market to unfairly benefit themselves. The Competition and Markets Authority (CMA) will look into how the social network gathers and uses certain data and whether it may provide an unfair advantage over rivals in the online classified ads and online dating space. As well as Facebook's advertising services, Facebook Login, a feature that allows people to sign into other websites and apps, will also form part of the probe.

What is artificial intelligence, and what are the job prospects in the field?

If you're considering a career change, it might be a good time to start looking for a good coding course. While many industries remain severely affected by the consequences of the COVID-19 crisis, there is one sector that is actively recruiting: jobs in AI are booming, and the trend is showing no sign of abating. A new report carried out by research agency Ipsos Mori into the current state of the UK's AI labor market found that close to 110,500 job opening were posted in the past year for roles related to AI and data science. That's more than double the number of vacancies registered in 2014, and a 16% increase from 2019, marking the highest year to date for AI jobs posted on the market. Every month for the past three years, between 8,000 and 10,000 roles were posted online, ranging from data analysts and software developers to research and development or even university positions such as lecturers and professors in AI and data science.

Important Note: The intention of this course is to teach MLOps fundamentals, core idea, its principles, standards etc and NOT Azure ML. Azure demo section is just included as a proof to show the working of an end-to-end MLOps project. "MLOps is a culture with set of principles, guidelines defined in a machine learning world for smooth implementation and productionization of Machine learning models." Data scientists have been experimenting with machine learning models from long time, but to provide the real business value, it must be operationalized i.e. push the models to production and measure their performance against business goals. Unfortunately, due to the current challenges and an non systemization in ML lifecycle 80% of the models never make it to production and remain stagnated as an academic experiment only.