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Machine Learning Syllabus Overview
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Module 1
Foundations for Practical Machine Learning
Supervised vs. Unsupervised Learning: A Practical Guide
Differentiate between supervised and unsupervised learning with practical examples.
Train-Test Split for Model Evaluation
Split a dataset into training and testing sets to evaluate model generalization.
3
Scaling Features for ML Models
Apply feature scaling (e.g., StandardScaler) and explain its importance for distance-based and gradient-based algorithms.
K-Fold Cross-Validation: Robust Evaluation and Tuning
Explain the purpose of k-fold cross-validation for robust model evaluation and hyperparameter tuning.
Evaluating Classification Models
Evaluate classifier performance using accuracy, precision, recall, and a confusion matrix.
Regression Performance: MSE and R-squared
Evaluate regression performance using Mean Squared Error (MSE) and R-squared.
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Dimensionality Reduction with PCA