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Sentiment analysis attempts to determine the emotional tone expressed in text. 

The kernel trick in Support Vector Machines allows:

Recall is concerned with the proportion of actual positives that are correctly identified. 

. Unsupervised learning uses labeled data for training.

The DIKW hierarchy represents the flow from Data → Information → Knowledge → Wisdom. 

Data cleaning involves removing duplicates and handling missing values.

The Open Data Movement promotes:

Normalization in data preprocessing adjusts values to a common scale without distorting differences in ranges.

Naïve Bayes classifiers does not assume conditional independence among features given the class. 

The “Why Data Matters” concept emphasizes that decisions should be based on facts rather than intuition.