Machine Learning Fundamentals Quiz Quiz

Assess your understanding of essential machine learning concepts, including algorithms, supervised and unsupervised learning, and foundational terms used in ML interviews. This quiz helps learners, candidates, and enthusiasts confirm their knowledge of basic machine learning principles and definitions.

1. Reason for Machine Learning

   Why was machine learning introduced instead of relying only on hardcoded rules in traditional programming?

   1. It allows systems to learn patterns from data instead of relying on manual rules.
   2. It always runs faster than traditional programs.
   3. It never requires any human intervention after setup.
   4. It guarantees perfect accuracy in all applications.

   Explanation: Machine learning was introduced to enable computers to identify patterns and make predictions from data, moving away from rigid, manually programmed rules. This makes systems more adaptable and scalable. Although speed, automation, and accuracy can improve with ML, none are universally guaranteed—speed and accuracy still depend on multiple factors, and some level of human input is required, especially for setup and monitoring.

2. Types of Machine Learning

   Which of the following is not a main type of machine learning algorithm?

   1. Supervised learning
   2. Unsupervised learning
   3. Reinforcement learning
   4. Subversive learning

   Explanation: Supervised, unsupervised, and reinforcement learning are the three core categories of machine learning algorithms. Subversive learning does not exist in the standard types; it is a distractor. While the first three refer to how algorithms receive and use data for learning, 'subversive' is an unrelated or incorrect term in this context.

3. Understanding Supervised Learning

   Which scenario best describes supervised learning in machine learning?

   1. Training a model to predict house prices using previous sales data and their prices.
   2. Grouping customers into clusters without knowing their labels.
   3. Learning through trial and error with rewards and penalties.
   4. Analyzing data without any defined purpose.

   Explanation: Supervised learning involves using labeled data, like previous sales and prices, to train a predictive model. Clustering customers without known labels is unsupervised learning. Trial and error with rewards and penalties refers to reinforcement learning, while analyzing data without direction is not characteristic of supervised learning.

4. Naive Bayes Assumption

   What does the 'naive' in 'Naive Bayes' refer to?

   1. The assumption that features are independent from each other.
   2. The use of a neural network for calculations.
   3. The inability to handle numerical data.
   4. The requirement for a large amount of training data.

   Explanation: The 'naive' in Naive Bayes refers to its assumption that all features are independent, which simplifies calculations. The algorithm does not inherently use neural networks, nor does it mean it can't handle numerical data. While more data can generally improve performance, this is not what 'naive' refers to.

5. Supervised vs. Unsupervised Learning

   Which statement correctly distinguishes supervised learning from unsupervised learning?

   1. Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.
   2. Supervised learning can only be used for clustering tasks.
   3. Unsupervised learning always predicts future values.
   4. Supervised learning is only used for sorting data alphabetically.

   Explanation: Supervised learning algorithms are trained on labeled data, whereas unsupervised learning works with unlabeled data to uncover hidden patterns. Clustering is a primary example of unsupervised, not supervised, learning. Neither supervised nor unsupervised learning is limited to sorting data alphabetically, and unsupervised learning does not always involve prediction.