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Machine learning is able to learn, train from data, and solve/predict complex solutions which cannot be done with traditional programming. It enables us with better decision making and solve complex business problems in optimized time. Recent advancements in AI have been propelled by machine learning, particularly its subset, deep learning. Additionally, compared to black-box agents like Dialogflow, developing our own machine learning models provides greater control, enabling continuous improvement over time.
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How do we train and evaluate a model?
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For example, a classification algorithm (classifier) might learn to predict whether a given email is spam or not, as illustrated below. This is a binary classification task, where the goal is to categorize the input data into two mutually exclusive classes. In this case, the training data is labeled with binary categories, such as "spam" and "not spam," "true" and "false," or "positive" and "negative." These labels guide the model in learning the differences between the two categories, allowing it to make accurate predictions when exposed to new data.
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You can do just the basic reading of the above, and there . There are also a more in-depth explanations on the following third pagespage. |
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