In machine learning, overfitting arises when a model tries to fit the training data so well that it cannot generalize to new observations. Thus, overfit models seem to be outstanding on training data but performs poor on new, previously unseen observations. The main reason of overfitting is model complexity. Regularization controls the model complexity by penalizing higher terms in the model. If a regularization terms is added, the model tries to minimize both loss and complexity of model.

During my studies in engineering school in computer science, I had the opportunity to launch two entrepreneurship projects related to Machine Learning. The first is a project on the translation of sign languages into written languages through bracelets and an automated translation system. This project continues under the name of SignBand. Following my departure, I started to train in Machine Learning. I have taken several online courses and participated in several competitions on Kaggle and hackathons.

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