Summary
In the first two parts of this chapter, you learned what techniques exist for you to explore and statistically analyze raw datasets and how to use them hands-on on a real-life dataset.
After that, you learned about the dimensionality reduction techniques you can use to visualize high-dimensional datasets. There, you learned about techniques that are extremely useful for you to understand your data, its principal components, discriminant directions, and separability.
Furthermore, everything you have learned in this chapter can be performed on a compute cluster in your Azure Machine Learning workspace, through which you can keep track of all the figures and outputs that are generated.
In the next chapter, using all the knowledge you've gained so far, you will dive into the topic of feature engineering, where you learn how to select and transform features in datasets to prepare them for ML training. In addition, you will have a closer look at labeling and how Azure...