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What are common mistakes to avoid with What Is Machine Learning

Intermediate

Answer

A common mistake is training on data that does not represent real world cases, which leads to poor predictions later. Another mistake is skipping proper testing, so the model looks good in training but fails in production.

Real-world example An email provider uses machine learning to sort spam from normal messages by learning from millions of past emails that were already labeled as spam or not spam.

Common follow-ups: What is supervised learning, What is the difference between AI and machine learning, What is a training dataset

Machine Learning Fundamentals Deep Learning Supervised & Unsupervised Learning