Applied Scientist interview prep

Explanation:

Supervised and unsupervised learning are two primary types of machine learning. In supervised learning, the algorithm is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns to predict the label from the input data. In unsupervised learning, the algorithm is used on data without labeled responses. Here, the goal is to infer natural structures present within a set of data points.

Key Talking Points:

• Supervised Learning:

  • Uses labeled data.
  • Goal: Predict outcomes or classify data points.
  • Common algorithms: Linear regression, logistic regression, support vector machines, neural networks.

• Unsupervised Learning:

  • Uses unlabeled data.
  • Goal: Discover hidden patterns or intrinsic structures.
  • Common algorithms: K-means clustering, hierarchical clustering, principal component analysis (PCA).

NOTES:

Reference Table:

Follow-Up Questions and Answers:

• Question: Can you give an example of a situation where unsupervised learning would be more suitable than supervised learning?

  • Answer: Unsupervised learning is more suitable for tasks like customer segmentation, where you want to group customers into distinct segments based on purchasing behavior without prior labels.

• Question: What are some challenges associated with unsupervised learning?

  • Answer: Challenges include determining the number of clusters or groups, evaluating the quality of results without labeled data, and dealing with the complexity and computational cost of algorithms.