refactor algorithm selection paragraph

modules/chapter1/pages/section1.adoc

@@ -6,7 +6,7 @@ A machine learning model is a program that can learn and make predictions or dec
 
 For example, in natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words. In image recognition, a machine learning model can be taught to recognize objects -- such as cars or dogs. A machine learning model can perform such tasks by having it __trained__ with a large dataset. During training, the machine learning algorithm is optimized to find certain patterns or outputs from the dataset, depending on the task. The output of this process -- often a computer program with specific rules and data structures -- is called a machine learning model.
 
-The process of creating a model includes steps for data collection and selection, the selection of an algorithm to process the data, training the model with such algorithm by tuning its hyperparameters, validating it, and exporting it so that it can be deployed in a later step.
+The process of creating a model includes steps for data collection and selection, the selection of an algorithm to process the data, the selection of an algorithm for training the model, tuning the training algorithm hyperparameters, validating it, and exporting it so that it can be deployed in a later step.
 
 In machine learning, a hyperparameter is a configuration setting that is not learned from the training data but is set prior to the training process. These settings are essential for controlling the behavior of a machine learning algorithm or model and can significantly impact the model's performance. Hyperparameters are essential in machine learning because they define the behavior and performance of a model. Proper selection and tuning of hyperparameters can be a crucial part of the machine learning workflow and can significantly affect the success of a model in solving a particular problem.