Summary
That was such a journey! Take a moment to recap what you have just learned. This chapter had four main topics: supervised learning, unsupervised learning, textual analysis, and image processing. Everything that you have learned fits into those subfields of machine learning.
The list of supervised learning algorithms that you have studied includes the following:
- Linear learner
- Factorization machines
- XGBoost
- KNN
- Object2Vec
- DeepAR forecasting
Remember that you can use linear learner, factorization machines, XGBoost, and KNN for multiple purposes, including solving regression and classification problems. Linear learner is probably the simplest algorithm out of these four; factorization machines extends linear earner and is good for sparse datasets, XGBoost uses an ensemble method based on decision trees, and KNN is an index-based algorithm.
The other two algorithms, Object2Vec and DeepAR, are used for specific purposes. Object2Vec is used...
