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De Morgan's Laws are principles from Boolean algebra, and they are relevant to various fields, including computer science and machine learning. These laws describe the relationship between logical operators "AND" and "OR" when applied to propositions. There are two De Morgan's Laws: 

i) De Morgan's First Law: 

¬(P∧Q) ≡ (¬P) ∨ (¬Q) 

This law states that the negation of the conjunction (AND) of two propositions is equivalent to the disjunction (OR) of the negations of the individual propositions. 

A script example of De Morgan's First Law is code:  

      

ii) De Morgan's Second Law: 

¬(P∨Q) ≡ (¬P)∧(¬Q) 

This law states that the negation of the disjunction (OR) of two propositions is equivalent to the conjunction (AND) of the negations of the individual propositions. 

A script example of De Morgan's Second Law is code:  

      

In machine learning, these laws may be applied in logical expressions, particularly when dealing with boolean conditions or logical operations. They are useful for simplifying logical expressions and improving the efficiency of algorithms or models. For example, in feature engineering or data preprocessing, De Morgan's Laws may be applied to simplify complex logical conditions or expressions involving multiple features. 

De Morgan's Laws, along with the Distributive Property, can be extended to many complex logical formulas (page3625).

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