- A quick guide on feature scaling
Often in machine learning, we come across an imbalanced, non-normal, and skewed dataset. What does that mean and how does that impact our machine learning model?
In this article, we will cover the impact of imbalanced and skewed data on the ML model and ways to handle this data during the data preprocessing phase in order to enhance the performance of our model. Before we dive into the ways to deal with such datasets, let’s explore data preprocessing and what imbalanced and non-normal data means.
It is the feature engineering part of machine…
Researchers and data scientists often come across situations where they either do not have the real data or can not make use of it due to confidentiality or privacy concerns. To overcome this problem, synthetic data generation is carried out to create a replacement of real data. For the right functioning of the algorithm, the right replacement of real data needs to be done which further should be realistic in nature. The study presented in this article is with respect to the growing demand for synthetic data in Artificial Intelligence and how we can generate this data.
Synthetic data is…
Data Scientist || MLOps Engineer || Co-author of book “Applications of Reinforcement Learning to Real-World Data (2021)” || AI Tutor & Mentor