ML Use-Cases Quiz

Predicting housing prices based on features like location, square footage, and the number of bedrooms.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

This involves labeled data where the housing price is the target variable, making it a regression problem

Grouping customers into distinct segments based on their purchasing behavior.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

No labeled outcomes are provided; the goal is to group customers into clusters based on similarities in behavior.

Teaching a robot to play chess by rewarding it for winning and penalizing it for losing.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

The robot learns by interacting with the environment (playing chess) and receiving feedback in the form of rewards or penalties

Identifying fraudulent transactions in banking by using labeled data of fraudulent and non-fraudulent activities.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

Fraudulent and non-fraudulent labels are available, making this a classification task.

Recommending movies to users based on their past ratings and viewing history.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

Techniques like matrix factorization or deep neural networks analyze user behavior to predict preferences.

Reducing dimensionality of data to identify meaningful patterns or relationships in unlabeled data.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

The task involves finding structure or patterns without labeled outcomes, often using PCA or t-SNE.

Training a self-driving car to navigate roads by learning from real-world feedback.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

The car interacts with its environment and learns optimal policies based on rewards and penalties (e.g., staying in the lane, avoiding obstacles).

Detecting spam emails using a labeled dataset of spam and non-spam emails.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

The dataset contains labeled examples (spam or non-spam), making this a classification problem.

Clustering cities based on their population growth, economic activities, and geographic features.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

There are no predefined labels; the goal is to group cities based on shared characteristics.

Predicting disease outcomes using patient historical medical data and treatment results.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Answer explanation

Historical data with known outcomes is used to train a model for prediction.