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DataWrangling and Cleaning Interviewers may present candidates with messy datasets and evaluate their ability to clean, preprocess, and transform data into usable formats for analysis. What is cross-validation, and why is it used in Machine Learning?
Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about datawrangling and the importance of data quality.
Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.
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