[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],["อัปเดตล่าสุด 2025-07-27 UTC"],[[["\u003cp\u003eMachine learning models require all data, including features like street names, to be transformed into numerical (floating-point) representations for training.\u003c/p\u003e\n"],["\u003cp\u003eNormalization is crucial for optimizing model training by converting existing floating-point features to a specific range.\u003c/p\u003e\n"],["\u003cp\u003eWhen dealing with large datasets, selecting a relevant subset of data for training is essential for model performance.\u003c/p\u003e\n"],["\u003cp\u003eProtecting user privacy by excluding Personally Identifiable Information (PII) from datasets is a critical consideration.\u003c/p\u003e\n"]]],[],null,["Machine learning models can only train on floating-point values.\nHowever, many dataset features are *not* naturally floating-point values.\nTherefore, one important part of machine learning is transforming\nnon-floating-point features to floating-point representations.\n\nFor example, suppose `street names` is a feature. Most street names\nare strings, such as \"Broadway\" or \"Vilakazi\".\nYour model can't train on \"Broadway\", so you must transform \"Broadway\"\nto a floating-point number. The [Categorical Data\nmodule](/machine-learning/crash-course/categorical-data)\nexplains how to do this.\n\nAdditionally, you should even transform most floating-point features.\nThis transformation process, called\n[**normalization**](/machine-learning/glossary#normalization), converts\nfloating-point numbers to a constrained range that improves model training.\nThe [Numerical Data\nmodule](/machine-learning/crash-course/numerical-data)\nexplains how to do this.\n\nSample data when you have too much of it\n\nSome organizations are blessed with an abundance of data.\n\nWhen the dataset contains too many examples, you must select a *subset*\nof examples for training. When possible, select the subset that is most\nrelevant to your model's predictions.\n\nFilter examples containing PII\n\nGood datasets omit examples containing Personally Identifiable Information\n(PII). This policy helps safeguard privacy but can influence the model.\n\nSee the Safety and Privacy module later in the course for more on these topics.\n| **Key terms:**\n|\n- [Normalization](/machine-learning/glossary#normalization) \n[Help Center](https://support.google.com/machinelearningeducation)"]]
