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GATE DA Machine Learning

Syllabus structure & chapters

Complete syllabus outline for GATE DA Machine Learning. Click on any chapter below to explore subtopics, key concept highlights, expected questions, and start practicing mock tests specifically customized for GATE DA preparation.

Supervised Learning

Linear and Logistic Regression, Decision Trees and Random Forests, SVM and Kernel Methods, Neural Networks Basics: Perceptron, Backpropagation, Bias-Variance Tradeoff, Regularisation, Cross-Validation

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Unsupervised Learning and Evaluation

K-Means and Hierarchical Clustering, PCA and Dimensionality Reduction, Model Evaluation Metrics: Precision, Recall, ROC, Ensemble Methods Basics

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