Certificate in Natural Language Processing using Python

Natural Language Processing using Python

About Natural Language Processing using Python

Making genuine human language accessible to computer systems is the goal of the area of natural language processing (NLP). You may use the Python library NLTK, or Natural Language Toolkit, for NLP. A large portion of the data that you could be examining is unstructured and contains text that can be read by humans. 

Why is Natural Language Processing using Python important?

Python is a great option programming language for an NLP project due to a number of factors. This language is a great option for applications requiring NLP because of its straightforward syntax and transparent semantics. For many downstream applications, such as speech recognition or text analytics, NLP is crucial because it provides helpful quantitative structure to the data and assists in resolving linguistic ambiguity.

Who should take the Natural Language Processing using Python Exam?

• Python developers
• Beginners in Natural Language Processing
• Data Scientist

Natural Language Processing using Python Certification Course Outline

1. Regular expressions & word tokenization
2. Introduction to tokenization
3. Word tokenization with NLTK
4. More regex with re.search()
5. Advanced tokenization with NLTK and regex
6. Choosing a tokenizer
7. Regex with NLTK tokenization
8. Non-ASCII tokenization
9. Charting word length with NLTK
10. Named-entity recognition
11. Simple topic identification
12. Word counts with bag-of-words
13. Bag-of-words picker
14. Building a Counter with bag-of-words
15. Simple text preprocessing
16. Text preprocessing steps
17. Text preprocessing practice
18. Introduction to gensim
19. What are word vectors?
20. Creating and querying a corpus with gensim
21. Gensim bag-of-words
22. Tf-idf with gensim
23. Building a "fake news" classifier