Seja a primeira pessoa a gostar disto
If there is one crucial thing in building ML models, this would be the data preparation. That is the process of transforming raw data to a state where machine learning algorithms could be run to disclose insights and make predictions. Data preparation involves analysis, depends on the nature of the problem and the particular algorithms. As far as there are knowledge and experience involved, there is no such thing as automation, which makes the role of the data scientist the key to success.
ML is trendy and Microsoft already have more than 10 services to support ML. So we will focus on tools like Azure ML Workbench and Python for data preparation, review some common tricks to approach data and experiment in Azure ML Studio.