Feature Engineering for Machine Learning:

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by Alice Zheng, Amanda Casari

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists Alice Zheng, Amanda Casari ebook
ISBN: 9781491953242
Page: 214
Format: pdf
Publisher: O'Reilly Media, Incorporated

Understand machine learning principles (training, validation, etc. Basic knowledge ofmachine learning techniques (i.e. Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. Classification, regression, and clustering). Knowledgeable with Data Science tools and frameworks (i.e. ) Knowledge of data query and data processing tools (i.e. Buy the Paperback Book Feature Engineering For Machine Learning Models by Alice Zheng at Indigo.ca, Canada's largest bookstore. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. Originally a technique from statistics they have become an important tool in everyMachine learning engineer's tool kit. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). 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. A very good definition, elegant in its simplicity, is that feature engineering is the process to create features that make machine learning algorithms work. Basic knowledge of machine learning techniques (i.e. Common Principle component analysis; Low Variance Filter; High Correlation Filter; Random Forests; Backward Feature Elimination / Forward Feature construction.