AIBridge ML Data Scientist Interview Questions and Answers

Machine learning algorithms are used to train models on data to make predictions or decisions.

• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve an agent learning through trial and error.

• Examples of machine learning applications include image recognition, ...read more

Model evaluation techniques are used to assess the performance of a machine learning model.

• Common techniques include cross-validation, holdout validation, and bootstrap validation.

• Metrics such as accuracy, precision, recall, and F1 score can be used to evaluate model performance.

• Visualizations such as confusion matrices and ROC curves can also aid in model evaluation.

• It is important to use multiple evaluation technique...read more

Overfitting occurs when a model learns the training data too well, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

• Overfitting: Model is too complex, fits noise in the training data, performs poorly on new data

• Underfitting: Model is too simple, fails to capture underlying patterns in the data, performs poorly on both training and new...read more

LLM models, or Language Model Models, are a type of machine learning model that focuses on predicting the next word in a sequence of words.

• LLM models are commonly used in natural language processing tasks such as text generation, machine translation, and speech recognition.

• They are trained on large amounts of text data to learn the relationships between words and predict the most likely next word in a given context.

• Exa...read more