Machine Learning Quizzes and MCQs
Phase 1 Foundations & Basics Vocabulary & Math
ML Types Supervised, Unsupervised, RL Basics
01_TYPES
Data Preprocessing Missing values, Outliers, Train/Test Split
02_CLEAN
Feature Engineering One-Hot Encoding, Scaling, Normalization
03_FEAT
Evaluation Metrics Precision, Recall, F1, ROC-AUC
04_METRIC
Bias & Variance Overfitting, Underfitting, Regularization
05_LOGIC
Phase 2 Classical Algorithms The Toolbox
Linear & Logistic Gradient Descent, Cost Functions, Sigmoid
06_REGR
Trees & Ensembles Gini, Random Forest, XGBoost
07_TREE
SVM Kernels, Margins, Hyperplanes
08_SVM
Clustering K-Means, DBSCAN, Hierarchical
09_CLUS
Dim. Reduction PCA, t-SNE, LDA
10_PCA
Naive Bayes & KNN Probability & Distance Metrics
11_SIMP
Phase 3 Deep Learning Neural Networks
Phase 4 Applied ML Real-world Specialization