In this assignment, you are required to identify the correct answers to these question based on True / False statements. Noted by (T / F) Frame the Problem 1. The first question to ask is what exactly the business objective of the model. 2. Building a model is probably not the end goal, but how does the company expect to use and benefit from this model? 3. Knowing the objective is NOT important because it will determine how you frame the problem. 4. Knowing the objective is NOT important because it will determine which algorithms you will select. 5. Knowing the objective is NOT important because it will determine which performance measure you will use to evaluate your model. 6. Knowing the objective is NOT important because it will determine how much effort you will spend tweaking it. 7. A sequence of data processing components is called a data pipeline. 8. Pipelines are very common in Machine Learning systems, since there is NOT lot of data to manipulate and many data transformations to apply. 9. As a Data scientist, the first thing you should do is pull out your Machine Learning project checklist. 10. In building a model the Components typically run asynchronously. Each component pulls in a large amount of data, processes it, and spits out the result in another data store. A broken component CANNOT go unnoticed for some time if proper monitoring is not implemented. Root Mean Square Error (RMSE) is a good performance measure, but does NOT gives an idea of how much error the system typically makes in its predictions, with a higher weight for large errors. It is good practice to list and verify the assumptions that have been made so far (by you or others). Machine Learning algorithm, found interesting correlations between attributes, in particular with the target attribute. Machine Learning algorithms is to try out various attribute combinations. If the dataset is not too large, you can easily compute the standard correlation coefficient. Machine Learning algorithms. Instead of doing this manually, you should write functions for this purpose, for several good reasons: allow to reproduce these transformations easily on any dataset. Machine Learning algorithms. Instead of doing this manually, you should write functions for this purpose gradually build a library of transformation functions that you can reuse in future projects. Machine Learning algorithms. Instead of doing this manually, you should write functions for this purpose can use these functions in your live system to transform the new data before feeding it to your algorithms. Machine Learning algorithms. Instead of doing this manually, you should write functions for this purpose will make it possible for you to easily try various transformations and see which combination of transformations works best.