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What Is Supervised Learning? (Definition, Examples) | Built In

What Is Supervised Learning? (Definition, Examples) | Built In

Supervised learning is a type of machine learning that uses labeled data sets to train algorithms in order to properly classify data and predict outcomes.

Supervised Learning Examples

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15 reactions | Supervised Learning is an essential concept of Machine Learning. Therefore, it is crucial to understand the concept in-depth. Here is a step by step guide on how you can solve a supervised learning problem. Check out the full video to know more. #supervisedlearning #machinelearning #skillforlife | Coding Ninjas | Facebook

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Main Challenges in Machine Learning: 1. Insufficient or Poor-Quality Data Lack of labeled data for supervised learning. Noisy, incomplete, or biased data can lead to poor models. 2. Overfitting and Underfitting Overfitting: Model performs well on training data but poorly on new data. Underfitting: Model is too simple to capture the underlying pattern. 3. High Computational Cost Training complex models (e.g., deep learning) requires powerful hardware and GPUs. 4. Scalability Models trained on sma

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