Suppose you are a product manager, you want to classify customer reviews in positive and negative classes. Or As a loan manager, you want to identify which loan applicants are safe or risky? As a healthcare analyst, you want to predict which patients can suffer from diabetes disease. All the examples have the same kind…
Data anomalies in Machine Learning (ML) can pose significant challenges to the accuracy and reliability of models. Detecting and mitigating these anomalies are crucial processes for managing ML models and their applications. Data anomalies can arise from a variety of factors that are essential for data scientists and knowledge workers to handle: Human errors Feature inaccuracy Dataset…
Data Science is a domain that comprises many sub-domains such as artificial intelligence, machine learning, statistics, data visualization, and analytics as well as provides practical examples and exercises to help you apply these concepts in the real world. Over the past few years, there has been tremendous demand for data scientists. To improve business efficiency it…
Our machines are getting more intelligent and more capable of independent tasks, and they owe it to the rapidly growing fields of Artificial Intelligence and Machine Learning. However, both areas are incredibly complex and take time and effort to understand better. This article explores Regression vs. Classification in Machine Learning, including the definitions, types, differences, and use cases. The foremost…
