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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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Learning Book: Grokking Deep Learning
https://www.geeksforgeeks.org/introduction-machine-learning/
How do we train and evaluate a model?
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Training means building the model by learning patterns and parameters from the training dataset.
Testing involves assessing the model’s performance using the test dataset.
Hyperparameter tuning involves adjusting the model's hyperparameters to optimize its performance.
Model inference refers to using the trained model to make predictions or draw conclusions from new, unseen data. This step leverages the model’s learned patterns to apply it to real-world situations or new inputs.
https://www.w3schools.com/python/python_ml_train_test.asp.
What is Classification in Machine Learning?
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What is Classification in Machine Learning?
Classification is a supervised machine learning method where the model aims to predict the correct label or category for a given input. In classification, the model is trained using labeled training data, learns to identify patterns, and is then evaluated on test data to assess its performance. Once trained and evaluated, the model can be used to make predictions on new, unseen data.
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 are also a more in-depth explanations on the following third pages. |
A General Pipeline of Task-Oriented Spoken Dialogue Systems
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