This blog will focus on the growing category of Artificial Intelligence (AI). We will cover the latest discoveries, applications, and advancements in the field of AI. We will discuss topics such as machine learning, deep learning, natural language processing, robotics, and computer vision. We will also cover the implications of AI on our lives, from its applications in healthcare and education to its potential to revolutionize the way we interact with technology. As a blog, we will strive to provide an engaging, informative, and thought-provoking look into AI and its effects on our society.
What is Anomaly Detection Supervised Learning? Anomaly Detection Supervised Learning is an approach to identifying outliers – any data points that do not follow the assumed pattern or behaviour of the normally expected outcome. This methodology of anomaly detection looks at historical data which has been labeled and classified as either anomalous or non-anomalous in…
What is an Artificial Neural Network in Data Mining? An Artificial Neural Network (ANN) is a supervised learning algorithm modeled after the human brain. It can be used for data mining applications such as predicting stock market outcomes, medical diagnosis, and fraud detection. It consists of a network of interconnected nodes or neurons that are…
What is Outlier Detection and How is it Used in Python Sklearn? Outlier detection is the process of identifying observations, items, or entities that are significantly different from the majority of other related observations. It is an important tool for understanding the data distribution and can be used to detect abnormal patterns that may be…
What Is Knime Outlier Detection and How Can it Help Data Analysis? Knime Outlier Detection is a data analysis approach used to identify outliers in datasets. It can be used to analyze and detect whether extreme values, or anomalies, exist in a dataset. Knime Outlier Detection can help with data pre-processing and various downstream tasks…
Understanding Isolation Forest and Its Application in R Isolation Forests in R are a popular way to identify outliers or anomalies in a dataset. The algorithm is designed for unsupervised learning, meaning that the underlying data does not need to be labeled. Isolation Forests identify and isolate cases which are different from other items based…
