Enhancing NLP with Deep Neural Networks

In this session, you will gain a conceptual understanding of Natural Language Processing (NLP) techniques, and learn how to apply them through hands-on projects. You will begin with preprocessing steps necessary for parsing unstructured text data. You will then transform text using representations such as bag-of-words and TF-IDF. You will then perform topic modeling using an LDA model. Next, you will learn how Deep Neural Networks can be applied to NLP tasks, using word embeddings and RNN architectures. Finally, you will apply RNNs to perform sentiment analysis on movie reviews. The projects will be implemented in Python, and libraries like Scikit-Learn, NLTK and Keras, and starter code will be provided through GitHub.