Machine Learning Cheat Sheet 2026

The 30 highest-yield Machine Learning facts, distilled from real exam questions. Print it, save it as a PDF, or study it here — free, no sign-up.

1. In Machine Learning practice, what is the best approach to quality improvement in computer vision? → Use data-driven methods with measurable outcomes
2. Which of the following is NOT a hyperparameter of a Random Forest model? → Learned feature weights of the trees
3. In a confusion matrix for binary classification, what does a 'False Positive' represent? → A negative instance incorrectly classified as positive
4. What is the most important professional competency for Machine Learning certification in computer vision? → Deep knowledge combined with practical application skills
5. What is the primary consideration when implementing changes to neural networks? → Impact assessment and change management
6. Which of the following is a feature extraction example? → All of the above
7. In-text mining, which of the following approaches can be used for normalization? → Both B and C
8. What is 'spectral clustering' and what advantage does it have? → It uses eigenvectors of a similarity graph Laplacian to cluster data of non-convex shapes
9. Which approach best demonstrates mastery of classification in Machine Learning practice? → Applying principles to novel situations with sound judgment
10. How does reinforcement learning fundamentally differ from supervised learning? → RL learns from environment interaction and reward signals, not labeled examples
11. What does the term 'epoch' mean in the context of neural network training? → One complete pass through the entire training dataset
12. Which strategy is most effective for promoting student engagement in Machine Learning education? → Active learning with meaningful participation
13. What is the key innovation of Deep Q-Networks (DQN) over classic Q-learning? → Using a deep neural network to approximate Q-values for high-dimensional state spaces
14. What is the primary purpose of assessment in the Machine Learning context? → To gather information for informed decision-making
15. What is the purpose of active listening in Machine Learning professional interactions? → To fully understand the speaker's message and respond appropriately
16. Which dimensionality reduction technique is specifically designed to preserve local neighborhood structure? → t-SNE
17. Which of the following machine learning algorithms is based on the principle of bagging and is extensively used and effective? → Random Forest
18. What is 'co-clustering' (biclustering) in unsupervised learning? → Simultaneously clustering both rows and columns of a data matrix
19. In k-means clustering, what is the objective function being minimized? → Within-cluster sum of squared distances to centroids
20. What does validity mean when selecting an assessment tool for Machine Learning certification purposes? → The tool measures what it claims to measure
21. In logistic regression, what function maps the linear output to a probability between 0 and 1? → Sigmoid
22. What is the purpose of a rubric in Machine Learning assessment? → To provide clear criteria and consistent evaluation standards
23. Which assessment type is most useful for guiding ongoing instruction? → Formative assessment
24. What is the purpose of a sentence parser? → It is used to parse sentences to derive their most likely syntax tree structures.
25. What does the reward signal represent in reinforcement learning? → Immediate scalar feedback indicating how good an action was
26. What is the best practice for documenting assessment results in Machine Learning practice? → Document all findings objectively and completely
27. What is 'feature scaling' and why is it important for algorithms like SVMs and k-NN? → Normalizing feature ranges so no single feature dominates distance calculations
28. In the context of anomaly detection, which unsupervised approach assigns anomaly scores based on how easily a point can be isolated? → Isolation Forest
29. What is the fundamental principle behind feature engineering in the Machine Learning domain? → Balancing performance, reliability, and efficiency
30. Which approach best demonstrates mastery of regression in Machine Learning practice? → Applying principles to novel situations with sound judgment