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Show Notes
Feature Engineering for Machine Learning (Alice Zheng)
- Amazon USA Store: https://www.amazon.com/dp/1491953241?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Feature-Engineering-for-Machine-Learning-Alice-Zheng.html
- Apple Books: https://books.apple.com/us/audiobook/designing-machine-learning-systems-an-iterative/id1795798192?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Feature+Engineering+for+Machine+Learning+Alice+Zheng+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1491953241/
#numerictransformations #bagofwordsandngrams #categoricalfeaturehashing #principalcomponentanalysis #kmeansfeaturization #FeatureEngineeringforMachineLearning
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists is a practical technical guide by Alice Zheng and Amanda Casari, published by O'Reilly Media. It concentrates on a part of machine learning work that is often compressed into the label preprocessing: converting messy, heterogeneous raw information into representations that learning algorithms can use. Rather than surveying predictive algorithms in depth, the book examines how choices about data representation affect what a model can detect. Its examples use the Python data science stack, including NumPy, pandas, scikit-learn, and Matplotlib, making the material oriented toward implementation as well as concepts. The coverage spans numeric, categorical, text, and image data, then extends to feature selection and model-based construction of new variables. The organizing purpose is practical: readers learn not merely which transformation exists, but why scale, distribution, sparsity, frequency, and domain context shape its appropriateness. A concluding structured-data exercise connects separate techniques into a more realistic workflow.
- Amazon USA Store: https://www.amazon.com/dp/1491953241?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Feature-Engineering-for-Machine-Learning-Alice-Zheng.html
- Apple Books: https://books.apple.com/us/audiobook/designing-machine-learning-systems-an-iterative/id1795798192?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Feature+Engineering+for+Machine+Learning+Alice+Zheng+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1491953241/
#numerictransformations #bagofwordsandngrams #categoricalfeaturehashing #principalcomponentanalysis #kmeansfeaturization #FeatureEngineeringforMachineLearning
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists is a practical technical guide by Alice Zheng and Amanda Casari, published by O'Reilly Media. It concentrates on a part of machine learning work that is often compressed into the label preprocessing: converting messy, heterogeneous raw information into representations that learning algorithms can use. Rather than surveying predictive algorithms in depth, the book examines how choices about data representation affect what a model can detect. Its examples use the Python data science stack, including NumPy, pandas, scikit-learn, and Matplotlib, making the material oriented toward implementation as well as concepts. The coverage spans numeric, categorical, text, and image data, then extends to feature selection and model-based construction of new variables. The organizing purpose is practical: readers learn not merely which transformation exists, but why scale, distribution, sparsity, frequency, and domain context shape its appropriateness. A concluding structured-data exercise connects separate techniques into a more realistic workflow.
