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…
In real world, not every data we work upon has a target variable. This kind of data cannot be analyzed using supervised learning algorithms. We need the help of unsupervised algorithms. One of the most popular type of analysis under unsupervised learning is Cluster analysis. When the goal is to group similar data points in a…
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.
