2: Machine Learning Fundamentals (Supervised, Unsupervised, Reinforcement Learning, Linear Regression, Decision Trees, K-Means Clustering, Overfitting vs. Underfitting) — البرمجة والذكاء الاصطناعي الصف الثاني الثانوي لغات

درس «2: Machine Learning Fundamentals (Supervised, Unsupervised, Reinforcement Learning, Linear Regression, Decision Trees, K-Means Clustering, Overfitting vs. Underfitting)» فى مادة البرمجة والذكاء الاصطناعي لـالصف الثاني الثانوي لغات من وحدة «Artificial Intelligence and Machine Learning». This lesson details the core principles of Machine Learning. It explores the three fundamental paradigms: Supervised Learning (Regression and Classification on labeled data), Unsupervised Learning (Clustering and Dimensionality Reduction on unlabeled data), and Reinforcement Learning (reward maximization through trial-and-error). It covers foundational algorithms (Linear Regression, Decision Trees...

الدرس ده من وحدة «Artificial Intelligence and Machine Learning» فى مادة البرمجة والذكاء الاصطناعي لـالصف الثاني الثانوي لغات على منصة دروسات.

الدرس متاح بالاشتراك: ترم كامل أو المادة لوحدها.

ملخص الدرس

This lesson details the core principles of Machine Learning. It explores the three fundamental paradigms: Supervised Learning (Regression and Classification on labeled data), Unsupervised Learning (Clustering and Dimensionality Reduction on unlabeled data), and Reinforcement Learning (reward maximization through trial-and-error). It covers foundational algorithms (Linear Regression, Decision Trees, K-Means) and analyzes the Bias-Variance tradeoff, Overfitting vs. Underfitting, and model evaluation metrics.

هتتعلم إيه فى الدرس ده

• Machine Learning algorithms learn patterns from empirical data rather than rule-based coding.
• Supervised learning trains on labeled data for regression (continuous) and classification (discrete).
• Unsupervised learning finds hidden structures in unlabeled data (e.g., K-Means clustering).
• Reinforcement learning trains agents to maximize cumulative rewards via environmental interaction.
• Underfitting represents high bias (too simple); overfitting represents high variance (memorizing noise).
• Train/test splits, cross-validation, and regularization ensure robust model generalization.