What it does? 💡 A web application tool to automate and systematize trials at the Civil Engineering Laboratory. Why did you build this project? 💡 This was my thesis project for my professional degree.

What was my motivation? 💡 This project was conceived in collaboration with my thesis tutor (Phd. Alejandro Hidalgo). He was enchage for the labs and I had learned a web development tools. What did you learn? 💡 Process identification and characterization, information analysis, system design, and programming the logic behind the primary chosen trials, how to create a basic user guide, how to generate results in web view and PDF, hands on HTML & CSS & JavaScript, use of Django as the main framework, SQLite data base, get some basic statistics on the frequency of some trials. Use PythonAnywhere servers to deploy the web applications.

What it does? 💡 It's a Web Application for Testing Services at a Civil Engineering Laboratory. Why did you build this project? 💡 It will be used not only to put together the business's logistics but also to automate in real-time the logic behind some of the most used lab tests in the civil engineering career.

What was my motivation? 💡 This is built on top of my thesis project, so I'll implement some new UX/UI features and make an upgrade to the project architecture. I will create a package for extending and scaling the implemented tests, this will be updated and maintained on a different repo. What did you learn? 💡 The focus of this project is to create a Data-WareHouse to extract and create some nice Dashboards to give some insides about the characteristics of the tests and business financials.

What it does? 💡 To generate insights about Life Expectancy, we created a pipeline from the ETL to create a data warehouse using APIs from the World Bank, World Health Organization, and United Nations. With this clean data, we created stunning dashboards and predictions based on machine learning models. Why did you build this project? 💡 This is the final project for Henry's Data Science Bootcamp.

What was my motivation? 💡 Reduce the gap by three years between developed and underdeveloped countries in the next 10 years! What did you learn? 💡 Agile methodologies (SCRUM) and a GitHub flow to collaborate as a team. The pipeline was built using the architectures of "ETL with Airflow running as a web service," "Data Analytics with simple Notebooks," "Data Lake and Data Warehouse as a Service in Linode Cloud" "Interactive Dashboards using PoweBI and Streamlit for ML predictions," and "Docker for development and production environments".

What it does? 💡 AIt's a resume from Machine Learning Specialization Course from Stanford, the Jovian Course Machine Learning with Scikit-Learn: Zero to GBMs and the Deep Learning with PyTorch: Zero to GANs. Why did you build this project? 💡 It was partly a bootcamp project to achieve the highest possible score in the E-commerce dataset. But I'll use it as a kaggle notebook repository.

What was my motivation? 💡 I know Python very well, but I'd like to have a solid foundation in supervise and unsupervise learning. On the other hand, it is a good starting point for Deep Learning and AI. What did you learn? 💡 Libraries such as Pandas, Scikit-Learn, and PyTorch; Machine Learning supervised learning, unsupervised learning, recommender systems, and reinforcement learning. Best practices for Jupyter notebooks. However, the gem of this repo is practical advice for using learning algorithms.