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

Feature-Engineering-for.pdf
ISBN: 9781491953242 | 214 pages | 6 Mb

• Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
• Alice Zheng, Amanda Casari
• Page: 214
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781491953242
• Publisher: O'Reilly Media, Incorporated

Online audio book downloads Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists 9781491953242

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by Alice Zheng, Amanda Casari Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. 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. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you’ll also learn how to practice better data science. Learn exactly what feature engineering is, why it’s important, and how to do it well Use common methods for different data types, including images, text, and logs Understand how different techniques such as feature scaling and principal component analysis work Understand how unsupervised feature learning works in the case of deep learning for images

Feature Engineering for Machine Learning - O'Reilly Media
Feature Engineering for Machine Learning. Principles and Techniques for DataScientists. By Alice Zheng, Amanda Casari. Publisher: O'Reilly Media. Release Date: March 2018. Pages: 214  Deep learning - Wikipedia
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This basically reinforces what we mentioned earlier about data scientists spending close to 80% of their time in engineering features which is a difficult and Typically machine learning algorithms work with these numeric matrices or tensors and hence most feature engineering techniques deal with  Principal Machine Learning Engineer Job at Intuit in Greater Denver
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