Data Augmentation – Cherry-picking data in a useful and positive way
Data Pre-Processing techniques exist to ensure data quality and they can be grouped in different ways. There is no consensus on the best way to create these groups, although examples of such groups usually include data cleaning, feature transformation, feature...
Feature Learning – Helping Machine Learning algorithms derive value from data
Data Pre-Processing techniques exist to ensure data quality and they can be grouped in different ways. There is no consensus on the best way to create these groups, although examples of such groups usually include data cleaning, feature transformation, feature...
Unsupervised Learning – Wandering through the swamp of unlabelled data
A task-oriented approach to Machine Learning allows us to divide it into three categories: supervised learning (data are labelled according to some criteria), unsupervised learning (no labels exist for the data), and reinforcement learning (no data is directly available, only...
Feature Transformation – Making data features Machine Learning friendly
Data Pre-Processing techniques exist to ensure data quality and they can be grouped in different ways. There is no consensus on the best way to create these groups, although examples of such groups usually include data cleaning, feature transformation, feature...
Event Driven Architecture with Cosmos DB
Event Driven Architecture with Cosmos DB O Azure Cosmos DB disponibiliza um conjunto de funcionalidades avançadas que o tornam ideal para suportar a construção de soluções cloud-oriented assentes em arquitecturas de microserviços. Venha conhecer as capacidades do Azure Cosmos DB...
