A Machine Learning Engineer with intensive skills in Machine Learning and Deep Learning. With more than 3 years of experience on different projects that use ML and DL to solve real-world problems.
My daily job is to build Machine Learning pipelines, models and automatization of various processes. Also researching, designing, implementing and deploying scalable machine learning solutions. In addition, I usually spend my personal time on personal projects related to ML.
The project consists of carrying out web scraping the page of swiss tech After that, data has been analyzed
The project consists of create a class to generate graphs and also some test
App using the functionality provided by Streamlit. In this case my girlfriend need to solve a few riddles and if she does it will recieve a key secret to insert in the bottom. The end product is a voucher in PDF with the gift to her birthday
This a project that I appreciate because it was used to generate a gift to my girlfriend. Using DeepFaceLab a clip of Harry Potter was made changing harry potter's face for hers.. After this I decide use face recognition to get all the frames where Harry potter appeared in a specific movie and with this information automate the change of face in the first movie
Starting from a branch this was a project for the big data exploitation subject. The idea was using data from tennis matches try to predict who is the winning in each match. And with this information decide which was the best strategy of odds comparing with real values in betting house
Time series problem, without any extra feature. The target was to predict the consumption of water. To solve this problem data preprocessing was done, in addition new features was created. You can find here the code
In this competition, it was analyzed raw images and metadata to predict the “Pawpularity” of pet photos. In this project Docker and Pytorch were used to avoid any problem in relation with the environment between my colleague and me. You can find here the code
The competition consisted of problem of a computer vision, classification of seabed. To solve this problem was used the framework Pytorch Lightning. In addition, the library Timm was used to download pretrained models and apply transfer learning. You can find here the code
Time series problem, where the target was to predict the demand of a e-commerce. Moreover in this problem you have more features that only the target. To solve this problem data preprocessing was done, in addition new features was created. You can find here the code
This contest was promoted by the professor of the subject big data exploitation between the alumns. It is a regression problem where the study of the dataset, preprocessing and the preprocessing and the testing of the different models through cross validation was essencial to win.
Top 5 percentile of the class