Hispanics are one of the largest racial/ethnic minority groups in the United States and are disproportionately affected by health issues. Hispanic population datasets are not as available as for other ethnicities. Data Science (DS) is an interdisciplinary field that aims to extract knowledge and insights from structured and unstructured data. Artificial intelligence (AI) is an area of computer science, which considers building smart machines capable of "thinking". Machine Learning (ML) refers to analytical algorithms that iteratively learn from data. Thus, given the need for health disparities research with a focus on Hispanic health, there is an urgency on strengthen and enhancing the diversity of the NIH-funded workforce by utilizing DS for making Hispanics datasets Findable, Accessible, Interoperable, and Reusable (FAIR), and applying AI/ML approaches in this field to extract knowledge from Hispanic dataset to mitigate health disparities. In response to Notice of Special Interest (NOSI) "Administrative Supplements to Enhance Data Science Capacity at NIMHD-Funded Research Centers in Minority Institutions (RCMI)", we aim to enhance and build capacity for investigators and students in DS/AI/ ML topics such as Jupyter Hub, coding with R, RStudio and Python, using ML libraries and other cutting-edge techniques to address and mitigate Hispanic health disparities. We will develop a new course, "Applying Artificial Intelligence and Machine Learning to Health Disparities Research (AIML+HDR), focused on data analysis using Hispanic datasets, that represents multiple levels and domains of influence in the NIMHD Research Framework. This bilingual (Spanish and English) online asynchronous course will initially target mainly trainees at the University of Puerto Rico and other Hispanic institutions. The organization of the AIML+HDR will follow the data science project lifecycle and will be divided in two modules. Module I will focus on using DS to make the Hispanic datasets FAIR and include different modules for data understanding and data wrangling. Module II will add topics for creating AI/ML predictive models for diagnostic and treatments of Hispanic patients, including modules related to model planning and building phases. To develop examples and projects, we will use Hispanic datasets from public repositories such as the Surveillance Epidemiology and End Results Program (SEER) and All of Us project, and from private repositories such as AbartysHealth. This novel training course will diversity the NIH-funded data science workforce through the development of competencies and skills in Hispanic investigators and graduate students, who will then generate Hispanic datasets that are FAIR and apply AI/ML approaches to create relevant predictive models.