The goal of this chapter was to provide you with intuitive understanding of different standard ML algorithms so that you can make an informed choice. We covered the popular ML algorithms used for classification and regression.We also learnt how supervised and unsupervised learning are different from each other. Linear regression, logistic regression, SVM, Naive Bayes, and decision trees were introduced along with the fundamental principles involved in each. We used the regression methods to predict electrical power production of a thermal station and classification methods to classify wine as good or bad. Lastly, we covered the common problems with different ML algorithms and some tips and tricks to solve them.
In the next chapter, we'll study different deep learning models and learn how to use them to analyze our data and make predictions.