That may have been science fiction but scientists say the terrifying prophecy could soon become a reality. That's because new research warns that the Atlantic Ocean current which drives the Gulf Stream could collapse at'any time' from 2025 thanks to climate change. Known formally as the Atlantic Meridional Overturning Circulation (AMOC), the current is the driving force which brings warm water from the Gulf of Mexico up to the UK and is responsible for mild winters in Western Europe. If it collapsed, however, the impact would be devastating. Europe would be plunged into a deep freeze, while most of Africa, the Caribbean, and South American countries such as Colombia, Peru and Bolivia would experience rocketing temperatures.

Over the last decade, artificial intelligence has been a relentless source of business innovation. Over the last decade, artificial intelligence has been a relentless source of business innovation. Now its influence is about to expand dramatically. Once a staple of science fiction, AI has quietly forged a critical role in some of the most ordinary yet essential business tasks. Automation of business processes; data analysis; defect detection in manufacturing; basic interactions with customers -- all are an embedded part of modern business and increasingly enabled by AI.

Evaluating a model is just as important as creating the model in the first place. Even if you use the most statistically sound tools to create your model, the end result may not be what you expected. Which metric you use to test your model depends on the type of data you're working with and your comfort level with statistics. The following summary of model evaluation techniques is by no means exhaustive; it's intended to be a starting point if you're unfamiliar with the available techniques. In part 1, I discuss some of the common Statistical Tools and Tests.

Maybe we are interested in knowing what's likely to happen in each game they play. In this case, knowing the total number of home runs hit over the course of the season isn't going to be quite as helpful – to make an accurate forecast about the next game, we need to have game-level data. Is the game being played at home or away? The answers to these questions are all crucial to generating an accurate prediction of the Giants' next game. This type of forecast is called a'bottom-up' or'rollup'-based forecast because predictions are made for each game based on the Giants' probability of winning each matchup.

Face Recognition is a recognition technique used to detect faces of individuals whose images saved in the data set. Despite the point that other methods of identification can be more accurate, face recognition has always remained a significant focus of research because of its non-meddling nature and because it is people's facile method of personal identification. Face recognition algorithms classified as geometry based or template based algorithms. The template-based methods can be constructed using statistical tools like SVM [Support Vector Machines], PCA [Principal Component Analysis], LDA [Linear Discriminant Analysis], Kernel methods or Trace Transforms. The geometric feature based methods analyse local facial features and their geometric relationship.

Data science is a well-defined, serious field of study and work. But the term'data science' has become a bit of a buzzword. Yes, 'data scientists' have become increasingly important to many different types of organizations, but it has also become a trend term in tech recruitment. The fact that these words are thrown around so casually has led to a lot of confusion about what data science and data scientists actually is and are. I would formerly include myself in this group.

Regression analysis is a statistical tool to determine relationships between different types of variables. Variables that remain unaffected by changes made in other variables are known as independent variables, also known as a predictor or explanatory variables while those that are affected are known as dependent variables also known as the response variable. Linear regression is a statistical procedure which is used to predict the value of a response variable, on the basis of one or more predictor variables. Some common examples of linear regression are calculating GDP, CAPM, oil and gas prices, medical diagnosis, capital asset pricing etc. R Simple linear regression enables us to find a relationship between a continuous dependent variable Y and a continuous independent variable X. It is assumed that values of X are controlled and not subject to measurement error and corresponding values of Y are observed.

For the human eye reading a CV (resume) or a job ad is an easy task. These semi-structured documents are usually separated in sections and have layouts that makes it easy to quickly identify important information. In contrast, a computer system that parses CVs needs to be continuously trained and adapted to deal with the endless expressivity of human language. As a leader in the field of language technology, Textkernel is working hard to provide the best CV parser to our customers. In this blog article I will explain how we achieve this and discuss the focus of our current research efforts.