If you are interested in developing a product with me, feel free to explore the services page.

SapiensLink was created by a team of highly interdisciplinary individuals who are passionate about learning and sharing knowledge.

The main objective is to make complex educational content accessible and to empower individual creators to make their creations accessible to wider audiences without losing their ownership. In order to unlock this vision, we are planning to create a space where information can be consumed in different possible ways and Sapionauts can be provided with automatically generated tests to validate and prove newly acquired expertise.

If you are interested in joining our team, supporting us or have any feedback, please reach out to us at

Machine Learning Visualization

As part of any data science project, data visualization plays an important part in order to learn more about the available data and to identify any main pattern. Wouldn’t it be great to also make as visually intuitive as possible the machine learning part of the analysis?

In this course, we are going to explore some techniques that could help us to face this challenge, such as summarizing Hyperparameter Tuning searches, drawing ANNs graphs, creating visual interfaces for ML models and many more.

Population Modeling and Simulations in Python

One of the main limitations of the current state of machine learning and deep learning is the constant need for new data. How can we make predictions when we don’t have any data available? This lack of data is more common than we would normally think.

In this project, we’ll learn about modeling and simulations and how they can be applied in various population analysis scenarios, such as understanding how diseases can spread within a community, prey/predator dynamics, and Conway’s Game of Life. Throughout the project, we will make sure that everything is interactive and shareable on the web. To do so, we will learn to use libraries such as SciPy, Matplotlib, Plotly, Streamlit, Datapane, and others.

Applied Data Science in Tourism

Contributed to the publication of “Applied Data Science in Tourism” comprising Springer’s series ”Tourism on the Verge”. As part of this project, I covered the 12th chapter of the book, in which were discussed some of the most common Hyperparameter Tuning techniques used in Machine Learning and how they can be applied in various tourism related data-driven applications.



API as a product: Towards Data Science Article to Markdown Blog converter

Are you a writer for an online publication such as Towards Data Science and at the same time you run your own personal blog? If that’s the case, you probably had to go through the manual process of converting your articles from one platform to another.

In order to solve this problem, I created the “Towards Data Science Article to Markdown Blog converter” API. Using this API it can in fact be possible to start an automatic conversion of articles published on Towards Data Science into a Markdown format suitable for blogging platforms such as Jekyll and GitHub pages by just giving the program 3 input parameters:

  • The full name of the writer on Medium.
  • The username of the writer on Medium.
  • A link to the Towards Data Science article you want to convert to Markdown from Medium.

This Software as a Service is served over HTTP and hosted using an AWS Lambda function. If you are interested in trying the API yourself, a free tier of up to 20 requests per month is available on RapidAPI (alternatively paid plan options are also offered).

Notion Study Review System

Notion is a free application able to provide such as notes, kanban boards, calendars, etc.. As part of this product, I created a Notion template to help you guide you through your learning path (e.g. online courses, university, etc…). Making use of this template will help you to keep track of the activities completed and review for an exam. Additionally, the template acts as a built-in database in order to quickly access notes from past courses anytime after completion.

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