One of the most important elements determining the success of a machine learning model is Feature Engineering. In any machine learning project, feature engineering is considered to be the most time-consuming task and iterative part of a machine learning engineer's job, if done manually. Whether it is encoding categorical variables or creating interactions in terms of these tasks for feature engineering in machine learning can quickly become complex and resource-intensive.
However, these feature engineering challenges can be easily addressed by Python scripts. ML engineers can use it to automate and streamline these repetitive tasks. For example, smart categorical encoding scripts can automatically choose the best encoding techniques and handle rare and unseen categories easily. Similarly, automated numerical transformation is another feature engineering technique with Python that can evaluate multiple methods, like log or normalization, and select the best one.
In the following infographic, USAII® gives a comprehensive overview of 5 essential Python scripts that machine learning engineers can use to make their feature engineering in machine learning effective. With USAII® AI and machine learning certifications, professionals can learn different automated feature engineering techniques with Python scripts and make their projects highly efficient. As businesses today are heavily adopting AI and ML models, feature engineering has become a very important component.
Check out the infographic below and explore USAII® certifications to master feature engineering strategy for modern ML projects.
