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
- Regular expressions & word tokenization
- Introduction to tokenization
- Word tokenization with NLTK
- More regex with re.search()
- Advanced tokenization with NLTK and regex
- Choosing a tokenizer
- Regex with NLTK tokenization
- Non-ASCII tokenization
- Charting word length with NLTK
- Named-entity recognition
- Simple topic identification
- Word counts with bag-of-words
- Bag-of-words picker
- Building a Counter with bag-of-words
- Simple text preprocessing
- Text preprocessing steps
- Text preprocessing practice
- Introduction to gensim
- What are word vectors?
- Creating and querying a corpus with gensim
- Gensim bag-of-words
- Tf-idf with gensim
- Building a "fake news" classifier