C. b. You need to be very technical in order to understand the scores they generate The generate scores that are explicable, and you do not need any technical abilities in order to understand d. They generate very accurate predictions even though you can not explain the results 5. Al can help in customer relation management as follows: a. Predict customer behaviour and help with targeted marketing b. Classify customers into profitable and non-profitable so you can make informed decisions c. Help you predict which customers are likely to churn so you can launch a retention campaign d. All of the above. 6. In order to make your company good at Al you need to: Execute pilot projects b. Build an Al team c. Provide Al training d. Develop Al strategies e. Develop internal and external communications f. All of the above g. None of the above a Question 2 /2.5) For each of the following, please select whether you agree or disagree with the statement 1. If you would like to implement a Machine Learning algorithm in your industry, Labeled data is used during training. However, during scoring, the trained model is used to score unlabeled data a. True b. False 2. Machine Learning and Data Science projects work in the same way. Both of them train a model that is used in order to help prediction in the future a. True b. False 3. If you train several models, the best model to select for deployment is the one that minimizes the training error such that the training error is almost zero. a. True b. False 4. In order to implement a Machine Learning or Al project in your business, you need to have a business problem that technology is going to solve. As we know, Al is not the answer to every single business question. a. True b. False 5. Neural networks can be more accurate as compared to Decision Trees. The main downside of Neural Networks is that, despite providing accurate prediction, they fail to explain the results they produce