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Book Overview & Buying
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Table Of Contents
Solutions Architect's Interview
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Creating a robust ML model begins long before any algorithmic magic occurs. It starts with data preprocessing and feature engineering, two critical phases that lay the groundwork for successful ML implementations. In these stages, raw data is transformed into a refined format that ML models can understand and use effectively to make accurate predictions or decisions.
Data is the foundation of ML Models. This initial step involves addressing missing values, removing duplicates, and correcting errors in the dataset. It's like selecting raw ingredients and ensuring they're quality before cooking. Normalization and Standardization techniques scale the data to a standard range or distribution, eliminating biases due to varying scales of features. Data transformation can include encoding categorical variables, discretizing continuous variables, or creating polynomial features. This step ensures that the data is in a format...
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