Example response for question delving into Machine Learning with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence

•  Definition: Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the input data is accompanied by the correct output. Unsupervised learning, on the other hand, is a type of machine learning where the model is trained on unlabeled data, meaning the input data does not have any corresponding output
•  Objective: Supervised learning aims to learn a mapping function from the input variables to the output variable, allowing the model to make predictions on new, unseen data. Unsupervised learning, on the other hand, aims to discover hidden patterns or structures in the input data
•  Training Process: In supervised learning, the model is trained using a labeled dataset, where the input data and corresponding output are provided. The model learns from this labeled data to make predictions on new, unseen data. In unsupervised learning, the model is trained using an unlabeled dataset, and it learns to find patterns or group similar data points together without any explicit guidance
•  Examples: Examples of supervised learning algorithms include linear regression, logistic regression, and support vector machines. These algorithms are used for tasks such as classification and regression. Examples of unsupervised learning algorithms include clustering algorithms like k-means and hierarchical clustering, as well as dimensionality reduction techniques like principal component analysis (PCA) and t-SNE
•  Evaluation: In supervised learning, the model's performance is evaluated by comparing its predicted output with the true output from the labeled data. Common evaluation metrics include accuracy, precision, recall, and F1 score. In unsupervised learning, evaluation is more subjective and depends on the specific task. For clustering algorithms, metrics like silhouette score or cohesion and separation measures can be used
•  Use Cases: Supervised learning is commonly used in applications such as spam detection, sentiment analysis, and image classification. Unsupervised learning is used for tasks like customer segmentation, anomaly detection, and recommendation systems
•  Data Requirements: Supervised learning requires labeled data, which can be time-consuming and expensive to obtain. Unsupervised learning, on the other hand, can work with unlabeled data, making it more flexible and scalable
•  Limitations: Supervised learning relies on the availability of labeled data, which may not always be available or may be costly to obtain. Unsupervised learning, while more flexible, may not provide explicit insights or predictions as it operates without labeled data
•  Combination: In some cases, supervised and unsupervised learning can be combined. This is known as semi-supervised learning, where a small amount of labeled data is used in conjunction with a larger amount of unlabeled data to improve the model's performance

  What the Interviewer is trying to find out about you and your experiences through this question

•  Knowledge: Assessing the candidate's understanding of fundamental concepts in machine learning
•  Experience: Evaluating the candidate's practical experience in applying supervised and unsupervised learning algorithms
•  Problem-solving: Testing the candidate's ability to identify appropriate learning methods based on the nature of the problem
•  Communication: Assessing the candidate's ability to explain complex concepts in a clear and concise manner

  How to avoid some common minefields when answering this question in order to not raise any red flags

•  Confusing or incorrect definitions: Providing inaccurate or unclear definitions of supervised and unsupervised learning
•  Lack of understanding of use cases: Not being able to explain the typical use cases for supervised and unsupervised learning
•  Inability to differentiate between the two: Failing to highlight the key differences between supervised and unsupervised learning
•  Limited knowledge of algorithms: Not being familiar with common algorithms used in supervised and unsupervised learning
•  Failure to mention evaluation metrics: Neglecting to discuss the evaluation metrics used in supervised and unsupervised learning

 Related

  Other questions asked for the Data Scientist in Technology function. View details for the Data Scientist here

• How do you handle missing data in a dataset?
• How do you handle imbalanced datasets?
• How do you select the optimal number of clusters in K-means clustering?
• What is the purpose of a validation set in machine learning?
• Explain the bias-variance tradeoff.
• What is feature engineering and why is it important?
• Explain the difference between bagging and boosting.
• What is the purpose of dimensionality reduction techniques?
• How do you deal with multicollinearity in regression?
• How do you handle outliers in a dataset?
• What is the difference between precision and recall?
• Explain the difference between L1 and L2 regularization.
• What is the difference between classification and regression?
• What is the purpose of cross-validation?
• Explain the concept of overfitting and how to prevent it.
• What is the difference between bag-of-words and TF-IDF?
• What is regularization and why is it important?
• What is the curse of dimensionality?
• Explain the concept of A/B testing.