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 {{see also|Machine learning terms}}
-===Iteration in Machine Learning==
+==Introduction==
-Machine learning is a subfield of artificial intelligence that seeks to develop algorithms and statistical models that can learn from data and make predictions or decisions based on it. One key concept in machine learning is iteration; this concept helps ensure success when applying the learned algorithm or model.
+Iteration is a fundamental concept in machine learning that refers to the repeated execution of an operation over multiple cycles. This technique helps improve accuracy by tuning parameters until an optimal solution is found - in other words, iteration is simply repeating steps multiple times until one gets better results.
-Iteration is the process of repeating a task multiple times to improve outcomes. In machine learning, iteration is used to optimize parameters within models in order to minimize error or loss functions - which measure differences between predicted output and actual output, serving as an evaluation criterion for how well a model performs.
+==The Importance of Iteration in Machine Learning==
+Machine learning involves iteration, which is the process of optimizing parameters in a model to enable accurate predictions on new data. This involves adjusting the parameters based on errors made during training on a training dataset. By repeating this process multiple times, the model learns from its errors and improves its accuracy.
-Machine learning iterates using an optimization algorithm such as gradient descent or stochastic gradient descent. The optimization begins with an initial set of parameters and iteratively updates them based on the gradient of the loss function, which is the derivative of that loss function with respect to these same parameters. The gradient provides guidance on which direction updates should be made in order to minimize loss.
+One common application of iteration in machine learning is gradient descent, an optimization algorithm designed to find the minimum cost function. In gradient descent, model parameters are updated based on how well their parameters fit within a certain gradient.
-The iteration process continues until either the loss function reaches a minimum or some stopping criteria is met, such as an upper limit on iterations or a change in the loss function below a specific threshold. Once these parameters have been determined after each iteration, they can be used to make predictions or decisions regarding new data sets.
+==Types of Iterations in Machine Learning==
+Machine learning often employs several types of iterations, such as:
+Stochastic Gradient Descent (SGD): SGD allows the model to be updated using a randomly chosen subset of training data instead of using the entire dataset. This enables it to make rapid progress towards minimizing its cost function; however, this may introduce noise into the optimization process.
+Mini-batch Gradient Descent: Mini-batch gradient descent involves updating a model using a randomly chosen subset of training data to balance speed of convergence with stability in the optimization process. This minimizes errors associated with model updating.
+Batch Gradient Descent: With batch gradient descent, the model is updated using all of the training data. This form of gradient descent offers stability but may be computationally expensive for large datasets.
+==Explain Like I'm 5 (ELI5)==Iteration is the process of doing something over and over again, such as playing a game of tag to improve at it. In machine learning, iteration refers to when a computer program keeps trying to improve its accuracy in predicting things by altering its settings each time something goes wrong (like getting tagged in tag), then trying again. Over time, this helps the program get better at making predictions with increased practice.
 ==Explain Like I'm 5 (ELI5)==