In this chapter, we went through many things, such as, understanding NLP at a high level. There are various steps involved in NLP, such as text preprocessing, as well as techniques to carry this out, such as feature engineering and methods to perform feature engineering and classification or clustering of the feature vectors. We also looked into the linear SVM algorithm in which we went through the details of the SVM algorithm, the kernel function, and how it is more applicable to text classification.
We solved our problem using linear SVM in Core ML and we also saw a practical example of performing spam message detection using the linear SVM algorithm model that we developed in scikit learn and converted into a Core ML model. We wrote an iOS application using the converted Core ML model.
In the next chapter, we will be introduced to another ML framework, Fritz, which tries to solve the common problems that we see in model deployment and upgrades, and the unification of handling ML...