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Machine Learning Syllabus Overview
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Module 7
Advanced Linear Classifiers and SVMs
1
Sigmoid Function in Logistic Regression
Describe logistic regression's use of the sigmoid function to output probabilities.
Cross-Entropy Loss Intuition for Probabilistic Classifiers
Explain the intuition of cross-entropy loss for training probabilistic classifiers.
3
Logistic Regression: Training and Interpretation
Train a logistic regression model using scikit-learn and interpret its coefficients as log-odds.
SVM: Maximum-Margin Separators
Explain the core idea of a Support Vector Machine (SVM) as finding a maximum-margin separator.
5
C-Parameter and Kernel Trick in Non-Linear Classification
Describe the role of the C parameter (soft-margin) and the kernel trick (using the RBF kernel) for non-linear classification.
SVM with RBF Kernel: Hyperparameter Tuning
Train an SVM classifier with an RBF kernel using scikit-learn and tune its hyperparameters.
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Tree-Based Ensemble Models
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Image Classification with Neural Networks