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
Neural Net Basics Activation, Backprop, Perceptrons
12_ANN 
CNNs Convolution, Pooling, Filters
13_CNN 
RNNs & LSTMs Sequences, Vanishing Gradients
14_RNN 
Transformers Self-Attention, BERT/GPT Architecture
15_TRNS 
Phase 4 Applied ML Real-world Specialization
NLP Basics Tokenization, Embeddings, Word2Vec
16_NLP 
Reinforcement Learning Agents, Rewards, Q-Learning
17_RL 
Model Tuning Grid Search, CV, Hyperparameters
18_TUNE 
MLOps Deployment, Drift, Model Serving
19_OPS 
Ethics & XAI SHAP, LIME, Fairness, Explainability
20_XAI 