Testing your knowledge

Alright! You have just completed this chapter. Let's see if you have understood and retained the knowledge you have just acquired.

Take a look at the list of the following scenarios and determine which of the three ML types can be applied (supervised, unsupervised, or reinforcement):

1. There is a list of online feedback on products. Each comment has been labeled with a sentiment class (for example, positive, negative, or neutral). You have been asked to build an ML model to predict the sentiment of new feedback.
2. You have historical house pricing information and details about the house, such as zip code, number of bedrooms, house size, and house condition. You have been asked to build an ML model to predict the price of a house.
3. You have been asked to identify potentially fraudulent transactions on your company's e-commerce site. You have data such as historical transaction data, user information, credit history, devices, and network access data. However, you don't know which transactions are fraudulent.

Take a look at the following questions on the ML life cycle and ML solutions architecture to see how you would answer them:

1. There is a business workflow that processes a request with a set of well-defined decision rules, and there is no tolerance to deviate from the decision rules when making decisions. Should you consider ML to automate the business workflow?
2. You have deployed an ML model into production. However, you do not see the expected improvement in the business KPIs. What should you do?
3. There is a manual process that's currently handled by a small number of people. You found an ML solution that can automate this process, however, the cost of building and running the ML solution is higher than the cost saved from automation. Should you proceed with the ML project?
4. As an ML solutions architect, you have been asked to validate an ML approach for solving a business problem. What steps would you take to validate the approach?