Machine Learning Problem Types: Classification, Regression, Clustering and More! | AI for Beginners

Discover the key differences between supervised and unsupervised machine learning in this beginner-friendly guide! We’ll explore classification, regression, clustering, and anomaly detection problems, with real-world examples to help you understand each concept. Learn how labeled and unlabeled data define these problem types and test your knowledge with practical examples. Perfect for anyone diving into the world of machine learning!

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• Machine Learning in Plain English: https://youtu.be/yUifAhZ8qOs

🌟***TIMESTAMPS***🌟
00:00 – What’s the difference between supervised and unsupervised machine learning problems?
00:20 – Examples of classification (supervised learning) problems
00:45 – Defining classification problems in machine learning
01:03 – What does it mean to have labeled data in machine learning?
01:28 – Examples of regression (supervised learning) problems
02:01 – Defining regression problems in machine learning
02:13 – Examples of clustering (unsupervised learning) problems
02:32 – Defining unsupervised learning and unlabeled data
03:03 – Defining clustering problems in machine learning
03:20 – Examples of anomalies in machine learning
03:35 – Defining anomaly detection (unsupervised learning) problems
03:36 – Example 1: What type of machine learning problem is this?
04:21 – Example 2: What type of machine learning problem is this?
04:54 – Example 3: What type of machine learning problem is this?