Basic Information
Course Name: Theoretical Aspects of Natural Language Processing
Course Language: English
Course Platform: Udemy
Course Instructors: Tracy Renee
Course Score: 4.1/5
Related Post: AI Free Courses – SetMyAI
Theoretical Aspects of Natural Language Processing
Theoretical Aspects of Natural Language Processing is a course that introduces natural language processing techniques and discusses the development of natural language processing technology and artificial intelligence. The course will cover three libraries of Python, namely the Natural Language Toolkit (NLTK), Spacey, and Sklearn.
Natural Language Toolkit (NLTK) includes:
- Processing text data
- Removing frequently used words
- Sentence tokenisation
- Word tokenisation
- Blank line tokenisation
- Frequency distribution
- Stop words
- Unikgrams, bigrams, trigrams, and ngrams
- Stemming
- Lemmatisation
- Part of speech tagging
- Named entity recognition
- Chunking
- Chinking
Spacey is a new library for natural language processing, which includes:
- Lemmatisation
- Part of speech tagging
- Named entity recognition
- Displacy
- Pattern matching
Sklearn is a machine learning library for Python, which includes:
- CountVectorizer
- TfidfTransformer
- Cosine similarity
- TfidfVectorizer
- HashingVectorizer
- DictVectorizer
To conduct sentiment analysis, the course will also use classifiers such as LinearSVC from the Sklearn library and NaiveBayes.
Course Review by SetMyAI
Natural language processing theory will help students understand the theoretical foundations of natural language processing technology and how to apply machine learning and deep learning in Python. Students will learn how to match neural networks and classifiers with natural language processing techniques. SetMyAI recommends students interested in natural language processing technology to study this course.
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