[major=AI | course_id=NLP301c | syllabus_id=12549 | semester=6 | combo=core | title=Natural Language Processing]
Course ID: NLP301c
Title EN: Natural Language Processing
Title VI: Xử lý ngôn ngữ tự nhiên
Credits: 3.0
Prerequisites: AIL30x and (PRP201c or PFP191)
Description: Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.
By the end of this Specialization, students will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots.
Student tasks: Student must get the certification of Natural Language Processing Specialization from Coursera to be accepted to the final examination
Tools: 
Course type: coursera_only
Decision: 359/QĐ-ĐHFPT dated 04/09/2025
Approved date: 2025-04-09
CLO1: Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words.
[1] category=PE; type=Final exam; weight=50.0; duration=120'; question_type=; grading_guide=All studied courses.; completion_criteria=; clos=4
[2] category=TE; type=Final exam; weight=50.0; duration=60'; question_type=; grading_guide=All studied courses. Each module of course contributes 2-3 questions.; completion_criteria=; clos=4
[1] desc=https://learner.coursera.help/hc/en-us/articles/208280036-Coursera-Code-of-Conduct; author=; publisher=Coursera; edition=; isbn=; main=; online=True; note=
[2] desc=https://www.coursera.org/learn/classification-vector-spaces-in-nlp?specialization=natural-language-processing; author=; publisher=Coursera; edition=; isbn=; main=True; online=True; note=MOOC 1: Natural Language Processing with Classification and Vector Spaces
[3] desc=https://www.coursera.org/learn/probabilistic-models-in-nlp?specialization=natural-language-processing; author=; publisher=Coursera; edition=; isbn=; main=True; online=True; note=MOOC 2: Natural Language Processing with Probabilistic Models
[4] desc=https://www.coursera.org/learn/sequence-models-in-nlp?specialization=natural-language-processing; author=; publisher=Coursera; edition=; isbn=; main=True; online=True; note=MOOC 3: Natural Language Processing with Sequence Models
[5] desc=https://www.coursera.org/learn/attention-models-in-nlp?specialization=natural-language-processing; author=; publisher=Coursera; edition=; isbn=; main=True; online=True; note=MOOC 4: Natural Language Processing with Attention Models
[6] desc=https://www.coursera.org/specializations/natural-language-processing; author=; publisher=Coursera; edition=; isbn=; main=True; online=True; note=SPEC: Natural Language Processing Specialization
Session 1: Specialization Introduction
Natural Language Processing with Classification and Vector Spaces
- Sentiment Analysis with Logistic Regression
Offline 1: Introduction to the online course NLP301c
Topic: Offline,Online; Teaching type: LO1; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)
All students should participate in offline slot (if any question)

Session 2: - Sentiment Analysis with Naïve Bayes
Topic: Online; Teaching type: LO1; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 3: - Vector Space Models
Topic: Online; Teaching type: LO1; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 4: - Machine Translation and Document Search
Topic: Online; Teaching type: LO2; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)
CLO2: Use locality sensitive hashing for approximate nearest neighbors.
Session 1: Natural Language Processing with Probabilistic Models:
- Autocorrect
- Part of Speech Tagging and Hidden Markov Models
Topic: Online; Teaching type: LO3; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 2: - Autocomplete and Language Models
Topic: Online; Teaching type: LO3; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 3: - Word embeddings with neural networks
Topic: Online; Teaching type: LO3; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 4: Natural Language Processing with Sequence Models:
'- Neural Networks for Sentiment Analysis
- Recurrent Neural Networks for Language Modeling
Topic: Online; Teaching type: LO4; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)
CLO3: Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.
Session 1: - Recurrent Neural Networks for Language Modeling
- LSTMs and Named Entity Recognition
Topic: Online; Teaching type: LO4; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 2: - Siamese Networks
Natural Language Processing with Attention Models:
- Neural Machine Translation
Topic: Online; Teaching type: LO4; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 3: - Text Summarization
- Question Answering
Topic: Online; Teaching type: LO5; LO: 
ITU: 
Download: - Watch all videos, read all materials in the MOOC
- Finish quizzes, self-assessment, assignments and take part in discussion boards (if needed)

Session 4: - Chatbot
Offline 2: Review Natural Language Processing Specialization
Topic: Offline,Online; Teaching type: LO5; LO: 
ITU: 
Download: https://flm.fpt.edu.vn/gui/role/student/- Watch all videos, read all materials in the MOOC- Finish quizzes, self-assessment, assignments and take part in discussion boards (if neededDeadline of Coursera completion: End of Friday of the week.All students should participate in review slot (if any question)
CLO4: Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.
CLO5: Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering and to build chatbots.