ML 1.8 Association Apriori algorithm

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2.1 Control statements in C programming (sequential & conditional)

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Control statements in C: - C is a structure-orientated programming language; the statements that are used to control the flow of execution of a program are called control structures. The statements provide a specific order to execute the C program. In C programming, we have four types of control structures. Sequential control structures Conditional control structures Iterative control structures Jumping control structures   1) Sequential control structures: - The C program is a collection of instructions or set of statements which are executed sequentially in a user-specific order. No statement is slipped, and no statement is executed more than once.                   Syntax: -                Flow chart: -                Example: - Output: - 1) Conditional control structures: - It is also known as selection, branchi...
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2.4 Arrays in c programming

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  Arrays: - An array is a collection of elements of the same data type stored in contiguous memory locations. It is a derived data type and a homogenous data structure. It is a variable which can store multiple values of the same type under a single variable name, making it easier to manage and manipulate related data. The size of an array is fixed. To access the elements of an array, we use indexes or subscripts accordingly. array index or subscript is used to identify array elements address. Array index or subscript starts with 0 to n-1.   Declaration of an array: - The declaration of an array means telling the compiler to create an array variable and reserving memory for a fixed number of elements of the same data type. Initialization of an array: - Initializing an array means assigning initial values to its elements. Types of arrays: - Arrays are classified based on the number of dimensions they have:             ...
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ML 1.2 Supervised Learning in Machine Learning

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  Supervised Learning in Machine Learning Supervised learning is a machine learning approach where a model is trained using labeled data .  Each training example includes input data and the correct output.  The model learns the relationship between inputs and outputs and then uses that learning to make predictions on new, unseen data. In simple terms, the system learns under guidance, similar to how a student learns from a teacher who provides correct answers. Supervised Learning Works Collect labeled data (input + correct output) Split data into training and testing sets Train the model using the training data Test the model on unseen data Use the model for real-time predictions Real-Time Examples of Supervised Learning 1. Email Spam Detection (Classification) Input : Email content, sender, keywords Output : Spam or Not Spam Real-time use : Gmail filters spam emails automatically based on past labeled emails. 2. Student Performance Prediction ...
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