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Using the site URL of the server as the key, the whole page title, content and URL from the page will be processed. If we add a few other non-standard pages to the list, the machine learning module will detect the current page and then extract its key (URL, content, and title) from the list. This will be the feature vector for the machine learning algorithm.
In this example, the output will be a list of URLs, titles and content that contains the features.

Then, every feature in the output list is used as a training vector for the machine learning algorithm. The whole feature is a training vector that we use to train the machine learning algorithm. It will be a feature vector in the machine learning algorithm.

Let’s go back to the greeting cards. We’ll use the machine learning algorithm for sentiment analysis.
The machine learning algorithm will process every training vector, which is a feature vector.

The machine learning algorithm will create a model and solve the problem. A model is a mathematical expression of the problem. This can be combined and compared with other models in order to determine the best model.

In machine learning terms, the output is a model. The name of the machine learning algorithm determines the type of model. Let’s choose the Naive Bayes classifier because it is easy to understand and it is suitable for a small data set.

We can classify items into one of the categories. For example, we can classify products into positive or negative sentiment. We use machine learning to do this by training a model to identify positive sentiment. But this will be done only for the items that we labeled as positive in the training data. We can also use the machine learning algorithm to classify the original data as positive or negative.

The next step is to use the model to classify the sentiment of the unclassified items. We create a test set in our data set. The test set is used to evaluate the accuracy of the model.
The test set is exactly the same as the training set. The first member of the test set is the same as the first member of the training set. The second

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