Applications of NLP
In this module, you will learn different applications that use NLP
You will learn how to preprocess text using NLP tools.
Hands on Parts of Speech (POS)
When you are dealing with languages it is normal that we will come across parts of speech. You will learn how you can tag POS in this module.
Regular Expressions Introduction
You may observe while reading an article or a newspaper that, between the words, you will find several regular expressions too. In this module, you will learn how to deal with regular expressions.
The technique of comprehending natural language the way humans communicate based on meaning and context is known as semantic analysis. Semantic technology analyses the logical structure of phrases to find the most important parts in a text and comprehend the subject at hand.
Deep Learning for Deep NLP
You can learn the introduction of deep learning for NLP. In case you are familiar with this already, it will be a refresher.
Introduction on Co- Occurrence matrix
A co-occurrence matrix will include certain entities in rows (ER) and column (CC) (EC). The aim of this matrix is to show how many times each ER and each EC occur in the very same context.
Word embedding is a term used in natural language processing (NLP) to summarize the representation of words for text analysis, which is in the form of a real-valued vector that encodes the meaning of the word and predicts the meaning of words that are close in the vector area.
Latent Semantic Analysis (LSA) is a mathematical process that is used to get insight into components. Topic Modeling is based on this method. The basic idea is to divide a matrix of what we've - terms and documents - into 2 distinct document topic and topic-term matrices.
Skip-gram is a kind of unsupervised learning technique for finding the most similar words for a specified word. Skip-gram is a strategy for predicting the context word for a target word.
Word2vec is a natural language processing technique. The word2vec method learns word connections from a huge corpus of text using a neural network model. Once trained, this can recognize synonyms and propose extra words for a sentence.
Glove Hands on
GloVe (Global Vectors for Word Representation) is also a method of creating word embeddings. It is based on word context matrices and matrix factorization methods. And then, we factorize this matrix to get a lower-dimensional matrix with every row corresponding to a vector representation of every word.
Project on Text Classification
You will learn to work on text classification projects. For example, How to find if an email is spam or not.
Introduction to Sentiment Analysis
You will learn to work on a Sentiment analysis project. For example, How is the movie? Did the customer like the product? etc.