Master this deck with 20 terms through effective study methods.
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Teaching computers to learn from data.
Supervised, unsupervised, and reinforcement learning.
Supervised learning uses labeled data; unsupervised does not.
Data is used to train models for predictions.
Analyzing data to discover patterns and insights.
It ensures the quality and accuracy of the data used.
Creating new features to improve model performance.
AI is the broad concept; ML is a subset of AI; DL is a subset of ML.
To identify the most relevant features for model training.
It performs well on training data but poorly on unseen data.
It measures the relationship between two variables.
To scale features to a similar range for better model performance.
Learning by trial and error, using rewards and penalties.
They can skew results and affect model accuracy.
To evaluate the performance of a classification model.
To optimize model performance by adjusting parameters.
Classification predicts categories; regression predicts continuous values.
It is used to train the model to make predictions.
To tune model parameters and prevent overfitting.
To evaluate the final model's performance on unseen data.