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Channel Posts
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| 15 | +3 Machine Learning and AI Foundations: Causal Inference and Modeling | 1 449 |
| 16 | Understanding Bias and Variance in Machine Learning
Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias).
Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance)
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| 17 | Popular Python packages for data science:
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7. Statsmodels: For statistical modeling and hypothesis testing.
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