About the Journal

The Sage Science Review of Applied Machine Learning is a peer-reviewed academic journal that publishes original research articles, reviews, and case studies in the field of applied machine learning. The journal aims to provide a platform for researchers, practitioners, and scholars to share their innovative research and applications in the field of machine learning.

The journal covers a broad range of topics related to applied machine learning, including machine learning algorithms and models, deep learning and neural networks, natural language processing, computer vision, and image processing, data mining and knowledge discovery, statistical learning, reinforcement learning, transfer learning, explainable and interpretable machine learning, and machine learning for healthcare, finance, social media, and other applications.

We welcome submissions from both academia and industry and encourage interdisciplinary research that combines machine learning with other fields such as engineering, biology, psychology, and economics. All submissions undergo a rigorous double-blind peer-review process, and accepted papers are published online and indexed in major scientific databases.

Our mission is to contribute to the advancement of applied machine learning and its impact on society. We aim to provide a platform for the exchange of ideas, the dissemination of knowledge, and the promotion of research excellence in the field of applied machine learning.