Parametric and non-parametric models form the bedrock of machine learning. 🤖 Parametric models, like linear regression, use fixed parameters to make predictions. 📈 They’re computationally efficient but rely heavily on assumptions. Non-parametric models, like KNN, adapt to data, offering flexibility for complex relationships but may require more data. 🧠 Choosing the right model depends on your data and the problem you aim to solve. 🤔 Remember, understanding these models empowers you to unlock the potential of machine learning and build effective solutions! 🚀
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