Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › Artificial Intelligence › Machine Learning & Data

Supervised Learning

concept · part of Machine Learning & Data

A machine learning paradigm that trains on labeled input-output pairs for tasks like classification and regression.

Inside Supervised Learning (8)

• Cross-validation — A technique to assess model performance by splitting data into k folds and training multiple times.
• Data splitting — Dividing data into training, validation, and test sets to evaluate model performance.
• Hyperparameter tuning — The process of optimizing hyperparameters like learning rate or regularization strength to improve model performance.
• K-nearest Neighbors — A classification algorithm based on the closest neighboring data points.
• Linear Regression — A regression algorithm that models the relationship between inputs and output as a linear function.
• Random Forests — An ensemble method combining multiple decision trees to improve robustness and reduce overfitting.
• Regularization — Techniques like L1/L2 that penalize large coefficients to prevent overfitting.
• Support Vector Machines — A classification algorithm that finds the optimal hyperplane to separate classes.

Connections

• Uses Azure ML Service
• Builds on Data splitting
• Uses Hyperparameter tuning
• Uses Cross-validation
• Related to Data drift
• Related to Interpretability
• Related to Reinforcement Learning
• Related to Unsupervised Learning

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