In this online course, you will be introduced to the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined.

This tutorial is an excerpt from "Deep Learning Essentials" by Wei Di, Anurag Bhardwaj, Jianing Wei and published by Packt. Use the code ORKDNA10 at checkout to get the recommended eBook for just $10 until May 31, 2018. Like in many other cases, the representation of the data, which is how the information is encoded and shown to machine learning algorithms, is often the most important and fundamental part in all pipelines of learning or AI. The effectiveness and scalability of the representation largely determine for the performance of the downstream machine learning model and application. As mentioned in the previous section, traditional NLP often uses one-hot encoding to represent the word in a fixed vocabulary and uses a BoW to represent documents.

In this online course, you will be introduced to the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined.