The dataset contains information about credit applicants. Banks, globally, use this kind of dataset and type of informative data to create models to help in deciding on who to accept/refuse for a loan. After all the exploratory data analysis, cleansing and dealing with all the anomalies we might (will) find along the way, the patterns of a good/bad applicant will be exposed to be learned by machine learning models. The goal is to train the best machine learning model to maximize the predictive capability of deeply understanding the past customer's profile minimizing the risk of future loan defaults. The metric used for the models' evaluation is the ROC AUC given that we're dealing with a highly unbalanced data.

AI could well have been nominated Person of the Year 2020 by Time magazine due to huge media attention, in-depth scientific scrutiny and hot policy and regulatory debates that swirled around the great opportunities and enormous risks it poses. However, in 2021 and beyond, we should not stop talking about AI. The goal of this whitepaper is to contribute towards an inclusive development of AI and help restore and strengthen trust between policymakers and the public. This calls for a greater effort to understand AI's effects more clearly and develop explainable and accountable algorithms. Furthermore, there is a need for strong evaluation frameworks that can assess not only the performance but also the performance and socio-economic impact of AI.

Hierarchical Clustering uses the distance based approach between the neighbor datapoints for clustering. Each data point is linked to its nearest neighbors. There are two ways you can do Hierarchical clustering Agglomerative that is bottom-up approach clustering and Divisive uses top-down approaches for clustering. In this tutorial, I will use the popular approach Agglomerative way. In order to find the number of subgroups in the dataset, you use dendrogram. It allows you to see linkages, relatedness using the tree graph. You will find many use cases for this type of clustering and some of them are DNA sequencing, Sentiment Analysis, Tracking Virus Diseases e.t.c. Popular Use Cases are Hospital Resource Management, Business Process Management, and Social Network Analysis. Here we are importing dendrogram, linkage, cluster, and cophenet from the scipy.cluster.hierarchy

The fight against fraud has always been a messy business, but it's especially grisly in the digital age. To keep ahead of the cybercriminals, investment in technology – particularly artificial intelligence – is paramount, says Ajay Bhalla, president of cyber and intelligence solutions at Mastercard. Since the opening salvo of the coronavirus crisis, cybercriminals have launched increasingly sophisticated attacks across a multitude of channels, taking advantage of heightened emotions and poor online security. Some £1.26 billion was lost to financial fraud in the UK in 2020, according to UK Finance, a trade association, while there was a 43% year-on-year explosion in internet banking fraud losses. The banking industry managed to stop some £1.6 billion of fraud over the course of the year, equivalent to £6.73 in every £10 of attempted fraud.

Weeks after a study revealed that Amazon warehouse workers are injured at higher rates than staff at rival firms, the company has revealed it's testing new robots designed to improve employee safety. The e-commerce giant has ingratiatingly named two of the bots after Sesame Street's Bert and Ernie. Bert is an Autonomous Mobile Robot (AMR) that's built to navigate through Amazon facilities. In the future, the company envisions the bot carrying large and heavy items or carts across a site, reducing the strain on its human coworkers. Ernie, meanwhile, is a workstation system that removes totes from robotic shelves and then deliveries them to employees.

For guidance on how healthcare organizations can leverage connected health technologies to support care anywhere initiatives and create a better experience for healthcare providers and patients, join us for the webinar, Driving Patient-Centric Care: Innovating Drug Development and Care Delivery with Connected Health Technologies, live on July 20 at 11 am EDT. COVID-19 showed how resourceful healthcare and life science organizations could be in the midst of a global pandemic. As the science of how the coronavirus worked and how to contain it evolved, those on the front line had to respond quickly to make course corrections. In many ways, they were fixing the plane while flying it. This was no small feat with so many lives on the line including patients, direct care health providers, first responders, and staff.

Jenée Desmond-Harris is online weekly to chat live with readers. Here's an edited transcript of this week's chat. Q. Starcraft slump: My boyfriend is a kind, caring, loving man, and I am mostly satisfied with our relationship. His main hobby is the online game Starcraft, and he spends maybe 10 to 15 hours a week on it, usually a game each evening. The problem is that if he loses a game, it can color his mood for days. There are usually like two to three days a month where he's down in the dumps because of this.

In April, the European Commission released a wide-ranging proposed regulation to govern the design, development, and deployment of A.I. systems. The regulation stipulates that "high-risk A.I. systems" (such as facial recognition and algorithms that determine eligibility for public benefits) should be designed to allow for oversight by humans who will be tasked with preventing or minimizing risks. Often expressed as the "human-in-the-loop" solution, this approach of human oversight over A.I. is rapidly becoming a staple in A.I. policy proposals globally. And although placing humans back in the "loop" of A.I. seems reassuring, this approach is instead "loopy" in a different sense: It rests on circular logic that offers false comfort and distracts from inherently harmful uses of automated systems. A.I. is celebrated for its superior accuracy, efficiency, and objectivity in comparison to humans.

Principal Component Analysis (PCA) is a Machine Learning algorithm used for various applications such as dimensionality reduction, data/image compression, feature extraction, and so on. The most common usage of PCA is dimensionality reduction (and we will see that in action below). PCA is basically used to extract/find patterns in a given dataset.

Model deployment is one of the most important skills you should have if you're going to work with NLP models. Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output for decision-making for a specific use case. There are different ways you can deploy your NLP model into production, you can use Flask, Django, Bottle e.t.c .But in today's article, you will learn how to build and deploy your NLP model with FastAPI. In part 1, we will focus on building an NLP model that can classify movie reviews into different sentiments.