AML401

Anna University

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ML basics
  • Summary

    ML basics

  • Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, K-Nearest Neighbors (KNN), Classification, Regression, Naive Bayes, K-Means Clustering, Unsupervised Learning with K-Means, Principal Component Analysis (PCA), Linear Regression, Decision Trees, Support Vector Machines (SVM), Cross-Validation, Model Evaluation, Overfitting, Underfitting, Hyperparameters, Accuracy, Precision, Recall, F1-Score, Confusion Matrix, Clustering, Feature Engineering, Data Preprocessing, Traini...
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