Summary
In this chapter, we looked at ML and its different subcategories. We explored SL, UL, and RL strategies and looked at situations where each one would come in handy.
Looking into linear regression, we were able to find relationships between predictors and a continuous response variable. Through the train/test split, we were able to help avoid overfitting our ML models and get a more generalized prediction. We were able to use metrics, such as RMSE, to evaluate our models as well.
In the next few chapters, we will be taking a much deeper dive into many more ML models and, along the way, we will learn new metrics, new validation techniques, and – more importantly – new ways of applying data science to the world.
