Akademik Dersler - İçerik

Can define a machine learning problem and prepare the data for training using Scikit-learn (train/test split).
Can interpret metrics such as the Confusion Matrix, Precision, Recall, and F1-Score.
Can explain the concept of the Bias-Variance Tradeoff and the effect of regularization (L1/L2).
Can explain Support Vector Machines (SVM) and the “kernel trick” concept.
Understand concepts such as Information Gain and entropy in Decision Trees.
Implement and compare ensemble models such as Random Forest and Gradient Boosting (XGBoost) using Scikit-learn.