Experienced in the fields of Artificial Intelligence, Data Science, Software Engineering, and Big Data. I specialize as Senior Machine Learning Engineer and provide consulting services for AI & Data related projects.

Highly motivated with a passion for exploring the intersections of Mathematics, Data, and Computer Science, and how they can be used to solve real-world problems.

The repositories you'll see here are mostly for my learning journey towards growing in my career in data science and machine learning engineering or simply fulfilling my quest for knowledge.

• ml-regression-project-california-housing: showcase my ability to produce a successful and resilient Machine Learning system from the EDA to the engineering phase. It also shows my consultant skills in all the cycles of a project of this kind.

• stock-price-prediction: analysis of multivariate time series, data, and feature engineering as a base to develop predictive models that can forecast future stock index value prices of an index based on historical data.

• computer-vision-classification-experiments: Computer Vision classification experiments training and testing Convolutional Neural Networks (CNNs) to analyze the impact of different parameters and the use of techniques such as feature extraction and transfer learning.

• real-time-detection-system: experiments for real-time object detection, decoy filtering and localization using YOLOv11, OpenCV and Faster R-CNN.

• scratch-nn: python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy to develop further understanding in the base concepts of neural networks and provide a foundation for building a Production-ready Python library.

• statistical-rethinking: repository to practice statistics with Python libraries learned in the book of the same name. (Abandoned atm, I feel my statistical skills are proficient for Data Science and ML, but maybe one day I will come to the basics again to learn a statistical framework)

I am a master's student in the Applied Artificial Intelligence online program at the Luleå University of Technology with the Neuromorphic Computing specialization. As this takes most of my time, in addition to working the learning paths below got stopped.

• Mix marketing modeling (learning currently). Work related, so the project cannot be shown.

• Bayesian Statistics (stopped), primarily through the book Statistical Rethinking, and some articles that you can find in towards datascience. At the moment this is my progress

• Time Serie Analysis (finished), I am mainly using any source that I find interesting from this repository which contains a lot of references to learn. Unluckily, any project related to TSA cannot be shown as there are used in my job :(.

• Neural Networks and Deep Learning (finished), by the Deep Learning Specialization of Coursera, and I have been enjoying being exposed to Linear Algebra and Deep Learning. My progress (First course, finished)

• Data Engineering with Apache Spark and Delta Lakes (finished). Acquiring a Data Engineer Professional Certificate from Databricks. I enjoy data modeling and creating data pipelines to support Data Science projects.

• MLOps, DevOps, and AWS SageMaker (finished). Learned and applied in all of the projects at work.
• Cloud (Azure, AWS) and CI/CD tools (Azure DevOps, GitHub Actions, AWS CodeBuild, CodeDeploy, and CodePipeline) (finished). Learned and applied in all of the projects at work.

Open source projects related to Environmental Intelligence to help to preserve nature or evaluate the state of it.

See more at my Linkedin profile.