0PP
A focused collection of questions on Machine Learning to sharpen your skills for technical interviews.
Classification Metrics (Accuracy, Precision, Recall, F1-Score)
the Support Vector Machine (SVM) algorithm and the kernel trick
Overfitting and Underfitting in models
the Gradient Descent algorithm
the purpose of Cross-Validation
Decision Trees and concepts like Entropy or Gini Impurity
the importance of Explainable AI (XAI) and methods like LIME or SHAP
the Random Forest algorithm
the Bias-Variance Trade-off
the AUC-ROC Curve
Linear Regression and its assumptions
Supervised vs. Unsupervised vs. Reinforcement Learning
Principal Component Analysis (PCA) for dimensionality reduction
Regression Metrics (MAE, MSE, RMSE)
Logistic Regression for classification
Classification Metrics (Accuracy, Precision, Recall, F1-Score)
the Support Vector Machine (SVM) algorithm and the kernel trick
Overfitting and Underfitting in models
the Gradient Descent algorithm
the purpose of Cross-Validation
Decision Trees and concepts like Entropy or Gini Impurity
the importance of Explainable AI (XAI) and methods like LIME or SHAP
the Random Forest algorithm
the Bias-Variance Trade-off
the AUC-ROC Curve
Linear Regression and its assumptions
Supervised vs. Unsupervised vs. Reinforcement Learning
Principal Component Analysis (PCA) for dimensionality reduction
Regression Metrics (MAE, MSE, RMSE)
Logistic Regression for classification