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Show Notes
Machine Learning Pocket Reference (Matt Harrison)
- Amazon USA Store: https://www.amazon.com/dp/1492047546?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Machine-Learning-Pocket-Reference-Matt-Harrison.html
- Apple Books: https://books.apple.com/us/audiobook/linux-the-quick-start-beginners-guide-unabridged/id1164191831?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Machine+Learning+Pocket+Reference+Matt+Harrison+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492047546/
#Titanicclassification #missingdatapreprocessing #regressionevaluationmetrics #dimensionalityreduction #scikitlearnpipelines #MachineLearningPocketReference
Machine Learning Pocket Reference: Working with Structured Data in Python is a compact technical guide by Matt Harrison, published by O Reilly, for practitioners who need a usable map of applied machine learning rather than a first introduction to programming or statistics. Its subject is supervised and unsupervised learning on tabular, structured data, using the Python ecosystem and particularly scikit learn style workflows. The book follows the practical sequence that determines whether a model is useful: inspect and clean data, prepare features, choose an appropriate method, evaluate results, and organize the work into reproducible pipelines. Classification is illustrated with the Titanic dataset, while regression, clustering, and dimensionality reduction broaden the reference beyond a single task. Harrison emphasizes code-oriented examples, tables, and concise explanations, reflecting the book's role as an aid during training and project work. It assumes readers can already work in Python, with familiarity with tools such as pandas being helpful. The result is a focused desk reference for common modeling decisions involving real-world structured datasets.
- Amazon USA Store: https://www.amazon.com/dp/1492047546?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Machine-Learning-Pocket-Reference-Matt-Harrison.html
- Apple Books: https://books.apple.com/us/audiobook/linux-the-quick-start-beginners-guide-unabridged/id1164191831?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Machine+Learning+Pocket+Reference+Matt+Harrison+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492047546/
#Titanicclassification #missingdatapreprocessing #regressionevaluationmetrics #dimensionalityreduction #scikitlearnpipelines #MachineLearningPocketReference
Machine Learning Pocket Reference: Working with Structured Data in Python is a compact technical guide by Matt Harrison, published by O Reilly, for practitioners who need a usable map of applied machine learning rather than a first introduction to programming or statistics. Its subject is supervised and unsupervised learning on tabular, structured data, using the Python ecosystem and particularly scikit learn style workflows. The book follows the practical sequence that determines whether a model is useful: inspect and clean data, prepare features, choose an appropriate method, evaluate results, and organize the work into reproducible pipelines. Classification is illustrated with the Titanic dataset, while regression, clustering, and dimensionality reduction broaden the reference beyond a single task. Harrison emphasizes code-oriented examples, tables, and concise explanations, reflecting the book's role as an aid during training and project work. It assumes readers can already work in Python, with familiarity with tools such as pandas being helpful. The result is a focused desk reference for common modeling decisions involving real-world structured datasets.
