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Data Encoding Practical.mkv
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1. Imbalanced dataset and redressal methodology
1.1 Introduction to ML
1.2 AI vs ML vs DL vs DS
1.3 Types of ML Styles
1.4 Train, Test and Validation
1.5 Underfitting and Overfitting, Bias and Variance
1.6 Missing value and its treatment
5. Feature Extraction and Scaling
7. Data Encoding - Copy
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