Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists Alice Zheng, Amanda Casari
Publisher: O'Reilly Media, Incorporated

This spotlight has caused many industrious people to wonder “can I be a data scientist, and what are the skills I would need?”. Basic knowledge of machine learning techniques (i.e. This is often These techniques are particularly useful when data is very scarce. Feature engineering is more difficult because it's domain-specific, while learners can be largely general-purpose. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and . Köp boken Feature Engineering forMachine Learning Models: Principles and Techniques for Data Scientists av Alice Zheng, Amanda Casari (ISBN 9781491953242) hos Adlibris.se. Understand machine learning principles (training, validation, etc. Videos 1-6 of Linear Algebra review from Andrew Ng's Machine Learning course (labeled as: III. Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. Intro to data science using Python focused on data acquisition, cleaning, aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. ) Knowledge of data query and data processing tools (i.e. Classification, regression, and clustering). Using domain knowledge to strengthen your predictive model or prescriptive model out of prediction can be both difficult and expensive. Feature engineering as an essential to applied machine learning. The meteoric growth of available data has precipitated the need for data scientists to leverage that surplus of information.