Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists Alice Zheng, Amanda Casari
Publisher: O'Reilly Media, Incorporated
Basic knowledge ofmachine learning techniques (i.e. But before we get into it we must define what a feature actually is. In my mind feature engineering encompasses several different data preparationtechniques. Mastery is about knowing precisely how something is done, having an intuition for the underlyingprinciples, and integrating it into the knowledge web of what we already know. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). Classification, regression, and clustering). Practitioners agree that the vast majority of time in building a machine learning pipeline is spent on feature engineering and data cleaning. They may mistake it for feature selection or worse adding new data sources. A very good definition, elegant in its simplicity, is that feature engineering is the process to create features that make machine learning algorithms work. Simple : feature engineering is what will determine if your project is going to success, not only how good you are on statistical or computer techniques. Knowledgeable with Data Science tools and frameworks (i.e.