Summary
That was such a journey! Let's take a moment to highlight what we have just learned. We broke this chapter into four main sections: supervised learning, unsupervised learning, textual analysis, and image processing. Everything that we have learned fits those subfields of machine learning.
The list of supervised learning algorithms that we have studied includes the following:
- Linear learner algorithm
- Factorization machines algorithm
- XGBoost algorithm
- K-Nearest Neighbors algorithm
- Object2Vec algorithm
- DeepAR forecasting algorithm
Remember that you can use linear learner, factorization machines, XGBoost, and KNN for multiple purposes, including to solve regression and classification problems. Linear learner is probably the simplest algorithm out of these four; factorization machines extend linear learner and are good for sparse datasets, XGBoost uses an ensemble method based on decision trees, and KNN is an index-based algorithm.
The...
