Скачать или смотреть 7. Foundations of Machine Learning

In this video, we explore the core foundations of machine learning—how computers learn patterns from data without being explicitly programmed. You’ll learn how different learning paradigms work, why they matter, and how we evaluate models to ensure they generalize well.

🔍 What You’ll Learn
📌 Supervised Learning

Learning from labeled data to map inputs to outputs

Difference between classification and regression

Real-world examples: spam detection, house price prediction, image recognition

Why training, validation, and testing matter

📌 Unsupervised Learning

Discovering hidden patterns in unlabeled data

Clustering with k-means and when to use it

Applications: customer segmentation, anomaly detection, pattern discovery

📌 Reinforcement Learning (RL)

How agents learn through rewards and punishments

Understanding Q-learning and value-based decision making

Examples: game-playing agents, robotics, autonomous systems

📌 Model Evaluation & Overfitting

The danger of overfitting and how it affects prediction

Regularization techniques to prevent overly complex models

Cross-validation for reliable performance assessment

Why evaluating models correctly is crucial for deployment

🧠 Why This Matters

Machine learning powers modern AI systems—from recommendation engines to autonomous vehicles. Understanding its foundations helps you:

Build better predictive models

Analyze data more effectively

Apply ML concepts across real-world applications

Whether you're new to ML or reinforcing your knowledge, this video gives you a strong, practical introduction.

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