IJMLTA is high quality peer-reviewed journal, where every article will be peer-reviewed by several experts in the field. Upon acceptance, articles will be published in the latest open volume.

The journal aims to be a global forum of, for, and by the community and offers:

• Rapid peer review under the expert guidance of a global Editorial Board.
• No color or page charges, free submission, and is free to access for the first one year of publication
• High visibility
• Opportunities to Societies / Conferences / Institutes / Laboratories /Corporate to ‘partner’ with the Journal and enjoy the benefit of planning and publishing issues in hot areas of research, without being under the pressure of publishing a full-fledge journal.

IJMLTA welcomes research and review papers related to Machine Learning Techniques and applications, but not limited to the areas of:

• Linear Regression
• Logistic Regression
• Decision Tree
• Support Vector Machine
• Naive Bayes
• K-Nearest Neighbour
• Random Forest
• Dimentionality Reduction Algorithms
• Gradient Boosting Algorithms

For a complete list of topics, please visit: Aims & Scopes