Summary

In this chapter, we took many of the ML concepts from Chapters 1 and 2 and put them into practice. We used clustering to find patterns in our data in an unsupervised manner, and you specifically learned a lot more about how K-means is used for clustering and how it works.

We then dived into SL, and you explored the linear regression class within scikit-learn and learned how to use metrics to measure the performance of a regression model.

Next, you learned how to use XGBoost to build a classification model and classify items in the iris dataset based on their features.

Not only did you put all of those important concepts into practice, but you also learned how to create and use Vertex AI Workbench-managed notebooks.

Additionally, you learned other important concepts in the ML industry, such as how decision trees work, how Gradient Boosting works, and how XGBoost enhances that functionality to implement one of the most effective ML algorithms in the industry.

That...