یکشنبه 27 خرداد 1397
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists Alice Zheng, Amanda Casari
Publisher: O'Reilly Media, Incorporated
Understand machine learning principles (training, validation, etc. ) Knowledge of data query and data processing tools (i.e. Click here for the lowest price! But before we get into it we must define what a feature actually is. Basic knowledge ofmachine learning techniques (i.e. Classification, regression, and clustering). They may mistake it for feature selection or worse adding new data sources. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). Basic knowledge of machine learning techniques (i.e. Knowledgeable with Data Science tools and frameworks (i.e. Feature Engineering for Machine Learning Models: Principles and Techniquesfor Data Scientists by Alice Zheng. In my mind feature engineering encompasses several different data preparationtechniques. Paperback, 9781491953242, 1491953241. Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive.