- Supervised Learning
- Classification
- Regression
- Time Series Forecasting
- Unsupervised Learning
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Semi-Supervised Learning
- Reinforcement Learning(ML)
- Deep Learning(ML)
- Transfer Learning(ML)
- Ensemble Learning
- Explainable AI (XAI)
- Bayesian Learning
- Decision Trees
- Support Vector Machines (SVMs)
- Instance-Based Learning
- Rule-Based Learning
- Neural Networks
- Evolutionary Algorithms
- Meta-Learning
- Multi-Task Learning
- Metric Learning
- Few-Shot Learning
- Adversarial Learning
- Data Pre Processing
- Natural Language Processing(ML)
Clustering QUIZ QUESTIONS
Question: 1
The goal of clustering is to-
Question: 2
Clustering is a-
Question: 3
Which of the following clustering algorithms suffers from the problem of convergence at local optima?
Question: 4
Which version of the clustering algorithm is most sensitive to outliers?
Question: 5
Which of the following is a bad characteristic of a dataset for clustering analysis-
Question: 6
For clustering, we do not require-
Question: 7
Which of the following is an application of clustering?
Question: 8
On which data type, we can not perform cluster analysis?
Question: 9
Netflix’s movie recommendation system uses-
Question: 10
The final output of Hierarchical clustering is-
Question: 11
Which of the step is not required for K-means clustering?
Question: 12
Which is the following is wrong?
Question: 13
Which of the following uses merging approach?
Question: 14
Which of the following is a method of choosing the optimal number of clusters for k-means?
Question: 15
When does k-means clustering stop creating or optimizing clusters?
Question: 16
Which of the following clustering algorithm follows a top to bottom approach?
Question: 17
Which algorithm does not require a dendrogram?
Question: 18
Which of the following clustering algorithms suffers from the problem of convergence at local optima?
Question: 19
For topic modeling what should we use?
Question: 20
What is a dendrogram?