Answer Posted / Zasmendar Singh

Some important data preprocessing techniques in Python Machine Learning include: 1) Missing Value Handling - replacing missing values with mean, median, or mode; using interpolation or regression. 2) Data Cleaning - removing duplicates, outliers, and noisy data. 3) Feature Extraction - converting raw data into meaningful features, like using PCA for dimensionality reduction or Bag of Words for text analysis. 4) Data Normalization/Standardization: scaling the range of features to improve model performance.

Tell us do you have research experience in machine learning?

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Do you have research experience in machine learning?

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Tell me what are the last machine learning papers you've read?

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What are standardization and normalisation? Give one advantage of each over the other?

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