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Methodology for Experiments with Machine Learning Algorithms

PHASES

Introduction to machine learning

Phase 0

Selection of the study topic

Phase 1

Data collection and preprocessing

Phase 2

Dataset split and computational environment selection

Phase 3

Algorithm application and validation

Phase 4

Knowledge application

Phase 5

PUBLICATIONS

If memla.mx was helpful to you, feel free to cite us:

Sánchez-DelaCruz, E., Loeza-Mejía, C.I., Pozos-Parra, P., (2023).

MEMLA: Methodology for Experiments with Machine Learning Algorithms. [In process]

About MEMLA

Motivation: since ancient time, when the true value of information was still unknown, human beings have used them for the most diverse purposes. Currently, the data processing has shown benefits in industry, science, technology, and the art. In this sense, there are various knowledge extraction methodologies from data, however, they have gaps in phases that make them unsuitable for various 21st-century knowledge domains. Due to the above, the project Methodology for Experiments with Machine Learning Algorithms (MEMLA) was born as a novel and widespread approach to project development in multiple disciplines. MEMLA was conceived for the first time by the Ph.D. Eddy Sánchez-DelaCruz (Scientist and Writer) after several years of experience implementing machine learning algorithms, where he identified the area of opportunity for a new line of research and application for the benefit of humanity in this digital age. Sánchez-DelaCruz is passionate about artificial intelligence applications. In this project, they also collaborate researchers and undergraduate, master's, and doctoral students.

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