Master machine learning from fundamentals to advanced topics
Comprehensive overview of machine learning fundamentals, basic terminology, model evaluation methods, and classical algorithms with practical watermelon examples.
Master linear regression, logistic regression, LDA, and advanced techniques including multi-class classification and handling class imbalance with real-world examples.
Master decision tree algorithms including ID3, C4.5, and CART. Learn information gain, Gini index, pruning techniques, handling continuous/missing values, and multivariate trees.
Journey from simple perceptrons to cutting-edge deep learning. Master backpropagation, CNNs, modern architectures (ResNet, VGG), transfer learning, and real-world applications.
Master SVM from maximum margin principles to kernel methods. Learn dual optimization, kernel tricks (RBF, polynomial), soft margins, SVR, and practical applications with solid statistical learning theory.