In recent years, both artificial intelligence (AI) and cryptocurrency have emerged as major technological forces. While they may seem like unrelated topics, they are actually deeply intertwined. IBM notes three shared values of blockchain, the technology that underlies most cryptocurrencies, and AI: authenticity, augmentation, and automation. One of the key ways that AI is being used in the world of cryptocurrency is through the application of anomaly detection. Anomaly detection, in simple terms, is the process of identifying unusual or abnormal patterns in data.

Imbalanced classification is a common problem in machine learning, particularly in the realm of binary classification. This occurs when the training dataset has an unequal distribution of classes, leading to a potential bias in the trained model. Examples of imbalanced classification problems include fraud detection, claim prediction, default prediction, churn prediction, spam detection, anomaly detection, and outlier detection. It is important to address the class imbalance in order to improve the performance of our model and ensure its accuracy. Notice that most, if not all, of the examples, are likely binary classification problems.

Banking, financial institutions & customers have been facing fraud for a very long time, in fact ever since the financial industry was created. The chances of fraud being attempted are almost guaranteed wherever money and/or private data are present. As the use of digitization and use of technology increases, it also increases the ways and means for fraudsters to leverage the same technology to commit fraud. Fraud detection identifies an actual or expected fraud that has or may take place using advanced technologies like AI, OCR, and ML to identify potential threats, mitigate risks, and prevent their recurrence. Banks, financial institutions & any other organization that deals with money, finance, or any other financial instrument need to implement strict measures, systems, and processes in place to detect fraud at an early stage or if possible before it takes place.

ONLINE TRAINING, with Dr. Mira ABBOUD fees 50$ or 2.000.000 LBP, via OMT 5 days - January 23, 24, 25, 26 & 27 6:00pm - 8:00pm (UCT +2) The training covers the following topics: Python basics (lists/tuples/dictionaries) Numpy Library (slicing, boolean indexing) Data Acquisition with Pandas (Series & Dataframes) Data Manipulation (filter, aggregation & grouping, Cross-tabulation) Data Visualization Introduction to Pre-processing (outliers detection, null values, features selection, dimensionality reduction, standardization) - we will cover one or two techniques of each. Build basic classification model in Python

Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone. In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI. You'll see some of the ways that AI can be used and explore the principles of responsible AI that can help you understand some of the challenges facing developers as they try to create ethical AI solutions. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals.

Anomaly Detection using Auto Encoder. “Azure Machine Learning Auto Encoder Anomaly Detection Sample” is published by Balamurugan Balakreshnan.

Anomaly detection in the context of data science is detecting a data sample that is out of the ordinary and does not fit into the general data pattern (or an outlier). This deviation can result from a rare occurrence or an outlying event. Identifying these samples, called anomaly detection, is an integral part of any monitoring system. Anomaly detection has been traditionally done manually by inspection, which is a tedious process typically done by experts with significant domain knowledge. Anomaly detection is used in a wide variety of applications.

Anomalies are troublesome and seem to have a mind of their own! They sneak up on your system and cause problems, leaving you frantically trying to figure out why you have suddenly got a customer with money deducted from their account, and a cafe reporting that they have not received a payment. Or, there has been a sudden surge in activity across your transaction servers, due to increased cyber attacks and not actual customers trying to make payments. A proper miscreant, and the worst part is: it usually has no obvious characteristics to identify it. Anomaly detection methods are now an integral part of many companies dealing with a large customer base, and can help expose undetected problems in systems by automatically identifying strange values, like long delays in receiving an OTP for a payment transaction.

Jose Lopez is an AI/ML researcher at Intel Labs. Here, he explores the use of machine learning for audio understanding and industrial predictive maintenance. Intel operates six wafer fabrication (fab) sites and four test manufacturing locations globally, producing, on average, five billion transistors per second. Keeping these fabrication facilities running smoothly is a top priority as malfunctions can be costly. In addition, ensuring chip manufacturing production targets are met is vital due to the fact that some expect the chip shortage to continue into 2023.

How do we need to detect outliers? How do we need to treat the outliers? An outlier is that datapoint or observation which behaves very differently from the rest of the data. If we are finding the average net worth of a group of people, and if we find Elon Musk in that group, then the complete analysis will go wrong because of just one outlier. This is a reason why outliers should be treated properly before building a machine learning model.