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Data Science Internship Prep
·
Module 4
Machine Learning: Core Concepts and Workflow
Bias-Variance Tradeoff & Model Generalization
Explain the bias-variance tradeoff and its effect on model generalization.
2
Loss Functions and Gradient Descent in Model Training
Describe the role of loss functions (MSE, Cross-Entropy) and gradient descent in model training.
3
Regularization to Prevent Overfitting
Explain how regularization (L1/L2) helps prevent overfitting.
4
Interpreting Learning Curves for Overfitting and Underfitting
Diagnose overfitting and underfitting by interpreting learning curves.
5
Train-Test-Validation Splits
Implement train-test-validation splits for reliable model evaluation.
6
K-Fold Cross-Validation for Robust Model Evaluation
Apply k-fold cross-validation to get robust estimates of model performance.
7
Choosing the Right Metrics for Regression and Classification
Select appropriate evaluation metrics for regression (MAE, RMSE) and classification (Precision, Recall, F1, ROC-AUC) tasks.
8
Understanding Conditional Probability and Bayes' Rule for Model Predictions
Explain conditional probability and Bayes' rule in the context of model predictions.
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Data Visualization and Exploratory Data Analysis (EDA)
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Feature Engineering and Preprocessing Pipelines