Your question is Core ML Concepts and Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Tell me about your understanding of core machine learning concepts like linear regression, multicollinearity, classification, RNNs, LSTMs, transformers, attention mechanisms, and LLMs.
Explain when each approach is appropriate, how you would train and evaluate it, and the tradeoffs between recurrent and attention-based architectures. Include a small Python implementation that demonstrates preprocessing, model training, and evaluation for a regression or classification task, plus how the design would extend to sequential data. Address multicollinearity, overfitting, data leakage, and suitable evaluation metrics.