Q1 Gradient Descent 1 Point If a function is ,then gradient descent is guaranteed to find the global minimum given an appropriately chosen learning rate. 0 convex O nonconvex O differentiable 0 increasing 0 decreasing Save Answer 02 Gradient Descent 1 Point True or False: If the learning rate is too small, then gradient descent may bounce back and forth without hitting the minimum. 0 True 0 False Save Answer 03 Feature Engineering 1 Point Which of the following is true about feature engineering? feature engineering solves the optimal parameters in ordinary least squares feature engineering is the process of transforming raw data to improve the accuracy of models feature engineering allows us to encode nonnumeric data as features to our model feature engineering allows us to capture domain specific knowledge in our data Save Answer 04 Common Feature Engineering Methods 1 Point Select feature engineering methods to transform data One hot encoding Gradient Descent adding a bias vector Ngram encoding