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Machine Learning Fundamentals

Supervised Learning: Training models with labeled data. Examples include classification and regression tasks.

Unsupervised Learning: Finding patterns in unlabeled data. Clustering and dimensionality reduction are common techniques.

Reinforcement Learning: Learning through trial and error with rewards and penalties. Used in robotics and game playing.

Feature Engineering: The process of selecting and transforming variables to improve model performance.

Model Evaluation: Using metrics like accuracy, precision, recall, and F1-score to assess model quality.

	Machine Learning Fundamentals

	Supervised Learning: Training models with labeled data. Examples include classification and regression tasks.

	Unsupervised Learning: Finding patterns in unlabeled data. Clustering and dimensionality reduction are common techniques.

	Reinforcement Learning: Learning through trial and error with rewards and penalties. Used in robotics and game playing.

	Feature Engineering: The process of selecting and transforming variables to improve model performance.

	Model Evaluation: Using metrics like accuracy, precision, recall, and F1-score to assess model quality.