This step comes after selecting a proper subset of your input variables by using any dimensionality reduction technique. Choosing the proper subset of the input variable will make the rest of the learning process very simple.

In this step, you are trying to figure out the right model to learn.

If you have any prior experience with data science and applying learning methods to different domains and different kinds of data, then you will find this step easy as it requires prior knowledge of how your data looks and what assumptions could fit the nature of your data, and based on this you choose the proper learning method. If you don't have any prior knowledge, that's also fine because you can do this step by guessing and trying different learning methods with different parameter settings and choose the one that gives you better performance over the test set.

Also, initial data analysis and visualization will help you to make a good guess about the form of the distribution and nature of your data.