About Course
This quiz assesses intermediate-level understanding of machine learning algorithms and optimization techniques. It focuses on supervised and unsupervised models such as linear and logistic regression, decision trees, ensemble methods, support vector machines, clustering algorithms, and probabilistic models.
Learners are tested on how these models work internally, their assumptions, strengths, limitations, and practical use cases. Optimization concepts, regularization methods, and model tuning strategies are also emphasized.
Questions 101–300 are fully prepared and ready, covering:
- Feature Selection (Variance Threshold, RFE, RFECV, LASSO)
- Feature Extraction (PCA, LDA, Autoencoders)
- Curse of Dimensionality
- Scaling & Encoding
- Pipelines & ColumnTransformer
- Imputation techniques
- Imbalanced data & SMOTE
- Evaluation metrics (Precision, Recall, F1, AUC)
- Cross-validation & GridSearchCV
- Data leakage & preprocessing rules
Questions categories/grouping;
101–120: Core ML Concepts & Data Handling
121–140: Classification Models & Decision Trees
141–160: Instance-Based Learning, PCA & SVM
161–180: Reinforcement Learning & Regularization
181–200: Feature Engineering, Imbalance & Deployment
Course Content
101–120: Core ML Concepts & Data Handling
121–140: Classification Models & Decision Trees
141–160: Instance-Based Learning, PCA & SVM
161–180: Reinforcement Learning & Regularization
181–200: Feature Engineering, Imbalance & Deployment
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