My ML/Data Portfolio#

In this place I concentrate resources related to my continuous learning journey related to AI/Data fields.

As a Machine Learning practitioner, I’m Interested in building intelligent systems to solve real-world problems.

I learned the importance of taking notes and organizing the knowledge that you are acquiring. Learning is not an single view of information, sometimes you have to go back en refresh your knowledge to be able to connect the dots with the wide fields of AI space.

I invite you to explore this place that I’m developing with great enthusiasm.

[Linkedin] [Github] [Devpost] [Kaggle] [Coursera] [GCP Skills Boost]

eavelardev@gmail.com

ML/Data Projects#

Epileptic seizure detector (2022) [repo]#

This project was created during the BioMedTech Baja Hackathon 2022 related to Medical Devices.

Our Team (MedCreators) proposed a device capable of detecting epileptic seizures, sending an alert and calling to emergency contacts. One of the main highlights of this project is the use of on-device machine learning to do the inference on the device without the need of sending raw data to external devices to process the IMU data.

epimldev

Machine Learning questions (2023) [site]#

This is a project where ML questions are collected from many learning sources I used during my ML journey.

The question options are randomized and the questions can also be randomized to avoid a link between consecutive questions.

The visual elements of the web page where extracted from the Machine Learning Crash Course by Google, doing some kind of reverse engineering.

The code to create the web page is implemented in this [notebook].

Check GCP Courses Updates [notebook]#

Google is constantly adding new content to its catalog, but you as a user of the platform don’t really know it.

These resources are also removed and renamed, so this notebook checked the content using web scraping.

New resources are compared against the last checkpoint, specifying a status for new, removed and renamed elements.

A data analysis is performed [notebook] to find out the most expensive resources (maybe most valuable content) and the most recent ML videos on the learning platform.

Content