Feature Engineering and Dimensionality Reduction with Python

Artificial Intelligence (AI) is indispensable these days. From preventing white–collar fraud, real–time aberration detection to forecasting customer churn, businesses are finding new ways to apply machine learning (ML). But how does this technology make accurate predictions? What is the secret behind the fail–proof AI magic? Let us start at the beginning.

The focus of the data science community is usually on algorithm selection and model training. While these elements are important, the most vital element in the AI/ML workflow isn t how you choose or tune algorithms but what you input to AI/ML. This is where Feature Engineering plays a crucial role. Feature Engineering is essentially the process in which you apply domain knowledge and draw out analytical representations from raw data, preparing it for machine learning. Evidently, the holy grail of data science is Feature Engineering.

So, understanding the concepts of Feature Engineering and Dimensionality Reduction are the basic requirements for optimizing the performance of most of the machine learning models. Sophisticated and flexible models are sometimes useless if applied to data with irrelevant features.

The course Feature Engineering and Dimensionality Reduction, Theory and Practice in Python has been crafted to reflect the in–demand skills today, helping you to understand the concepts and methodology with respect to Python. The course is: