Скачать или смотреть Supervised vs. Unsupervised Learning: A Complete Guide to ML Algorithms

Confused about which Machine Learning algorithm to use for your project? In this video, we break down the fundamental differences between Supervised and Unsupervised learning, exploring how they work, when to use them, and how to evaluate their success.
In this video, we cover:
• The Core Difference: Understand how Supervised learning relies on "labeled" input-output pairs (like teaching a child with flashcards), while Unsupervised learning finds hidden patterns in unlabeled data without explicit programming.
• Key Algorithms: From Regression and Classification (Supervised) to Clustering and Dimensionality Reduction (Unsupervised).
• Decision Factors: Learn how to choose the right model based on data size, task complexity, and scalability.
• Evaluation Metrics: How do you validate a model without ground truth? We discuss metrics like the Silhouette Score and the Calinski-Harabasz index.
• Advanced Methods: A look at Semi-supervised learning, Reinforcement learning, and real-world applications like Unsupervised Deep Tracking (LUDT)

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