#Feature_Engineering

[devsnews]

Data cleaning is identifying and dealing with errors or inconsistencies in raw data. This can involve correcting, deleting, or replacing incorrect or incomplete data. Feature engineering is using existing data to create new features or variables that can be used to help make predictions. This includes transforming existing variables, creating new variables from existing data, or combining multiple variables into new features. This article will discuss the importance of Data Cleaning & Feature Engineering and introduce five courses to improve our skills in this area.

[sqljoker]

Tweeted

Implementing #Deep_Learning Methods and #Feature_Engineering for Text Data: The GloVe Model https://t.co/C92SAvpag4 via @kdnuggets

— SQL Joker (@sql_joker) April 25, 2018