BSCS2004 · Knowledge Base
Ml Foundations Notes
14
Concepts
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Facts
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Procedures
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464 - Introduction to Machine Learning
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465 - Linear Regression with One Variable
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466 - Gradient Descent Variants
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467 - Multiple & Polynomial Regression
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468 - Logistic Regression & Classification Metrics
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469 - k-Nearest Neighbors (k-NN)
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470 - Decision Trees
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471 - Ensemble Methods- Bagging & Random Forest
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472 - Boosting- AdaBoost, Gradient Boosting & XGBoost
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473 - Support Vector Machines
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474 - Clustering- k-Means, Hierarchical, DBSCAN
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475 - Dimensionality Reduction- PCA & t-SNE
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476 - Model Evaluation & Cross-Validation
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