In this chapter, we covered how to set up a development environment for Go that is optimized for ML applications. We explained how to install an interactive environment, Jupyter, to accelerate data exploration and visualization using libraries such as Gota and gonum/plot.
We also introduced some basic data processing steps, such as filtering outliers, removing unnecessary columns, and normalization. Finally, we covered sampling. This chapter took the first few steps in the ML life cycle: data acquisition, exploration, and preparation. Now that you have read this chapter, you have learned how to load data into a Gota dataframe, how to use the dataframe and series packages to process and prepare the data into a format that is required by your chosen algorithm, and how to visualize it with gonum's plot package. You have also learned about different ways of normalizing...
