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There are several fundamental concepts that are important to understand in machine learning:
- Supervised learning: This involves training a model on labeled data, where the correct output is provided for each example in the training set. The model makes predictions based on this input-output mapping. Examples of supervised learning include linear regression and support vector machines.
- Unsupervised learning: This involves training a model on unlabeled data, and the model must find patterns or relationships in the data. Examples of unsupervised learning include clustering and dimensionality reduction.
- Reinforcement learning: This involves training an agent to interact with an environment in order to maximize a reward. The agent learns through trial and error and adjusts its behavior based on the consequences of its actions.
- Features: These are the input variables that are used to make predictions. In supervised learning, the features are used to predict the output label. In unsupervised learning, the features are used to discover patterns in the data.
- Labels: These are the output variables that are predicted in supervised learning. They are also known as target variables or class labels.
- Model: This is a mathematical representation of the relationship between the features and labels. A model is trained using a dataset and is then used to make predictions on new data.
- Training set: This is a dataset used to train a model. It consists of a set of input-output examples that the model uses to learn the relationship between the features and labels.
- Test set: This is a dataset used to evaluate the performance of a trained model. It consists of a set of input-output examples that the model has not seen before, and is used to see how well the model generalizes to new data.
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