The book is divided into three sections. We make a (perhaps arbitrary) distinction between machine learning methods and deep learning methods by defining deep learning as any kind of multilayer neural network (LSTM, bi-LSTM, CNN) and machine learning as anything else (regularized regression, naive Bayes, SVM, random forest). We make this distinction both because these different methods use separate software packages and modeling infrastructure, and from a pragmatic point of view, it is helpful to split up the chapters this way.
By Emil Hvitfeldt
The techniques and tools covered in Supervised Machine Learning for Text Analysis in R are most similar to the requirements found in Data Scientist job advertisements.
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Coursera - Johns Hopkins University