a). What is NLP ?
b). Applications of NLP
c). Basic Python – List
d). Basic Python – String
e). Basic Python – Functions
f). Installing NLTK
2. Text Wrangling and Cleansing
a). What is Text Wrangling?
b). Text Cleansing
c). Sentence Tokenization
d). Word Tokenization
g). Stemming vs. Lemmatization
h). Stop Words Removal
3. Part of speech Tagging
a). NLTK POS Tagger
b). Sequential Tagger
c). Named Entity Recognition (NER)
4. Buildings NLP Applications
a). Topic Modeling
b). Text Summarization
c). Sentiment Analysis
a). NLTK vs. SpaCy
Text mining and Natural Language Processing (NLP) are among the most active research areas. Pre-processing your text data before feeding it to an algorithm is a crucial part of NLP. In this course, you will learn NLP using natural language toolkit (NLTK), which is part of the Python. You will learn pre-processing of data to make it ready for any NLP application.
We go through text cleaning, stemming, lemmatization, part of speech tagging, and stop words removal. The difference between this course and others is that this course dives deep into the NLTK, instead of teaching everything in a fast pace.
This course has 3 sections. In the first section, you will learn the definition of NLP and its applications. Additionally, you will learn how to install NLTK and learn about its components.
In the second section, you will learn the core functions of NLTK and its methods and techniques. We examine different available algorithms for pre-processing text data.
In the last section, we will build 3 NLP applications using the methods we learnt in the previous section.
Specifically, we will go through developing a topic modeling application to identify topics in a large text. We will identify main topics discussed in a large corpus.
Then, we will build a text summarization application. We will teach the computer to summarize the large text and to summarize the important points.
Finally, we compare NLTK with SpaCy, which is another popular NLP library in Python. It’s going to be a very exciting course. Let’s start learning.