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…
Fine-tuning models have gained momentum with the advent of transfer learning, particularly in natural language processing (NLP). Instead of starting from scratch, practitioners tweak sophisticated models initially developed by tech giants like Google or Microsoft.
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…
