About the course
Statistical NLP is the precursor for processing text data. Before starting to work on Recurrent neural networks, Statistical NLP gives the brief on how to extract, process & clean text data.
Data modeling is an essential part of the data science pipeline. This, combined with the fact that it is a very rewarding process, makes it the one that often receives the most attention among data science learners. However, things are not as simple as they may seem, since there is much more to it than applying a function from a particular class of a package and applying it on the data available.
In order for a model to maximize its potential, it needs an information rich set of features. The latter can be created in various ways. Whatever the case, cleaning up the data is a prerequisite. In this course you will learn about how to extract important text features, cleaning the text so that you can start working on the text with the help of Recurrent Neural networks.
Skills you will gain
- Words Vectonizer
- POS tagging
Statistical Natural Language Processing
- Pre-processing in NLP-Tokenization, Stop words, Normalisation,stemming and lemmatization
- Pre-processing in NLP-Bag of words, TF-IDF as features
- Language Models Probabilistic models, N-gram model and channel model
- Hands-on demo_NLP Basics with NLTK
- Hands-on demo : Word Embeddings
- Applications : POS tagging, NER
- Hands-on demo : POS tagging with NLTK
- Hands-on demo : TF-IDF with NLTK
- Show more