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COURSE PLAN (AY 26-27)
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AS PER BCA SCHEME & SYLLABUS 2024-2025 ONWARDS GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY, NEW DELHI
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Session - 2026-27
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Programme CodeCourse CodeCourse NameProgramme Course TypeSemesterDepartment
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020BCA305TNatural Language Processing.BCACCTFifthDICT
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Contact Hours Per WeekTotal CreditsAssessment Marks
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LTPInternal (CIA)External
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4--44060
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Contact Hours Per Semester
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LTP
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400
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1.Objective: In this course, the learners will be able to develop expertise related to Natural Language Processing and their applications.
1. Understand NLP fundamentals, Python-based text processing, and its applications.
2. Apply text preprocessing, corpora access, representation methods 3. Exploring language modelling techniques 4. Acquire skills in tagging, classification, sentiment analysis, and apply to real-world challenges.
Course Outcomes
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2. Course Outcome & Mapping, Course Articulation
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Course Outcome PO1PO2 PO3 PO5PO6 PO7PO9PO10
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CO305T.1 : Understand NLP fundamentals, Python-based text processing, and its applications.
31-4-2-2
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CO305T.2: Apply text preprocessing, corpora access, representation methods
2223-1-2
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CO305T.3: Exploring language modelling techniques
3332-2-3
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CO305T.4: Acquire skills in tagging, classification, sentiment analysis, and apply to real-world challenges.
321222-3
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Course Articulation (Average)
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1 - (Low); 2 - (Moderate); 3 – (High)
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  3. Pre-Requisites
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1Python Programming Language
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4. Pedagogical approaches (aligned with Teaching Learning Strategies):
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S.NTeaching-Learning StrategyAligned Pedagogical Approach
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1Technology Based LearningFlipped Classroom
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2Learning through Problem-SolvingInquiry-Based Learning
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3Project Based LearningCollaborative Learning
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5. Pedagogy used
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S.NPedagogy used
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1Business Simulations, Role Plays, Gamified Quizzes, Scenario-Based Activities, Management Games
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2Peer Presentations, Student Seminars, Peer Evaluation
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3Group Discussions, Team Assignments, Brainstorming Sessions, Collaborative Projects, Case Discussions
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6. ICT Tools Used
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S.NICT Tools Used
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1Laptops/Projector (for lectures and demonstrations),Python (with libraries like NumPy, SciPy, Scikit-learn, NLTK) (for practical implementation).
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2Voyant Tools
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3Online Tools & Use of Virtual Lab
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4LMS (Moodle/Google Classroom) for assignments and assessments
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7. Lesson Plan
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Lecture No.Unit No.TopicMapping with COBT LevelReferences Other resources:Web link/Videos Links/You Tube links(Wherever Applicable)
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L11Introduction to NLPCO1Remember (L1), Understand (L2)TB1https://web.stanford.edu/~jurafsky/slp3/
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L21Overview of Natural Language ProcessingCO1Understand (L2)TB1https://www.nltk.org/book/
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L31History and evolution of NLPCO1Understand (L2), Apply (L3)TB1,TB2https://huggingface.co/learn/nlp-course
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L41 Applications of NLP in real-world scenariosCO1Apply (L3), Analyze (L4)TB2freeCodeCamp.org
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L51 Approaches to NLPCO1Apply (L3), Analyze (L4)TB2Approaches to NLP Videos
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L61Computing with Language CO1Apply (L3), Analyze (L4)TB2What is NLP? | Codebasics
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L71Texts and WordsCO1Understand (L2), Analyze (L4)TB1,TB2Hugging Face – The AI community building the future.
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L81 A Closer Look at PythonCO1Analyze (L4)TB1,TB2NLP Videos
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L91Texts as Lists of Words CO1Apply (L3)TB1,TB2Natural language processing Videos
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L101dictionaries CO1Apply (L3), Analyze (L4)TB1,TB2AI CH AI464 UV 16×9
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L111Ethical considerations and bias in NLPCO1Apply (L3), Analyze (L4)TB1,TB2https://web.stanford.edu/~jurafsky/slp3/
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L121Current research trends and emerging applications in NLPCO1Analyze (L4)TB1,TB2
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L131Introduction to corpora CO1Apply (L3), Analyze (L4)TB2https://huggingface.co/learn/nlp-course
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L142Introduction to text dataCO1Apply (L3), Analyze (L4)TB2
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L152Accessing Text Corpora CO2Remember (L1), Understand (L2)TB1,TB2https://web.stanford.edu/~jurafsky/slp3/
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L162Accessing Text Lexical ResourcesCO2Understand (L2)TB1https://www.nltk.org/book/
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L172Conditional FrequencyCO2Analyze (L4)TB1https://huggingface.co/learn/nlp-course
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L182 Lexical Resources CO2Understand (L2), Apply (L3)TB1https://web.stanford.edu/~jurafsky/slp3/
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L192WordNetLevel,CO2Understand (L2), Apply (L3)TB1,TB2https://www.nltk.org/book/
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L202NLP PipelineCO2Understand (L2), Apply (L3)TB2https://huggingface.co/learn/nlp-course
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L212StringsCO2Apply (L3), Analyze (L4)TB2
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L222Text Processing at the Lowest CO2Apply (L3), Analyze (L4)TB2Approaches to NLP Videos
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L232Text preprocessing techniquesCO2Analyze (L4)TB1,TB2What is NLP? | Codebasics
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L242TokenizationCO2Apply (L3), Analyze (L4)TB1,TB2Hugging Face – The AI community building the future.
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L252StemmingCO2Apply (L3), Analyze (L4)TB1,TB2
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L262LemmatizationCO2Analyze (L4)TB1,TB2Natural language processing Videos
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L273Language ModelingCO2Apply (L3), Analyze (L4)TB1,TB2AI CH AI464 UV 16×9
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L283Probability theory andCO3Understand (L2)TB1,TB2https://web.stanford.edu/~jurafsky/slp3/
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L293Statistical language models, CO3Understand (L2), Apply (L3)TB2https://www.nltk.org/book/
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L303Syntactic analysis CO3Apply (L3), Analyze (L4)TB2https://huggingface.co/learn/nlp-course
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L313parsing techniquesCO3Apply (L3), Analyze (L4)TB1,TB2freeCodeCamp.org
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L323Vector space models CO3Apply (L3), Analyze (L4)TB1https://web.stanford.edu/~jurafsky/slp3/
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L333One-hot encodingCO3Analyze (L4)TB1https://www.nltk.org/book/
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L343Bag-of-Words (BoW) modeCO3Understand (L2)TB1https://huggingface.co/learn/nlp-course
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L353l, TF-IDF (Term Frequency-Inverse Document Frequency) representationCO3Apply (L3), Analyze (L4)TB1,TB2https://web.stanford.edu/~jurafsky/slp3/
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L363n-gram modelsCO3Apply (L3), Evaluate (L5)TB2https://www.nltk.org/book/
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L373Training setsCO3Understand (L2)TB2https://huggingface.co/learn/nlp-course
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L383Test setsCO3Apply (L3), Analyze (L4)TB2
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L393EvaluatingCO3Analyze (L4)TB1,TB2Approaches to NLP Videos
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L403 SmoothingCO3Analyze (L4)TB1,TB2What is NLP? | Codebasics
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L413
NLP Tasks and Techniques
CO3Apply (L3), Analyze (L4)TB1,TB2Hugging Face – The AI community building the future.
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L424
NLP Techniques
CO4Remember (L1), Understand (L2)TB1,TB2NLP Videos
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L434
Part-of-speech tagging
CO4Understand (L2)TB1,TB2Natural language processing Videos
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L444
Named Entity Recognition (NER)
CO4Understand (L2), Apply (L3)TB1,TB2AI CH AI464 UV 16×9
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L454
Sentiment analysis
CO4Apply (L3), Analyze (L4)TB2https://web.stanford.edu/~jurafsky/slp3/
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L464
Supervised Text Classification
CO4Apply (L3), Analyze (L4)TB2https://www.nltk.org/book/
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L474EvaluationCO4Analyze (L4)TB1,TB2https://huggingface.co/learn/nlp-course
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Naïve Bayes Classifiers
CO4Analyze (L4)TB1freeCodeCamp.org
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L494ChatbotCO4Understand (L2), Apply (L3)TB1,TB2https://web.stanford.edu/~jurafsky/slp3/
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L504
Case study: Students will work on a real-world NLP problem
CO4Apply (L3), Analyze (L4)TB1,TB2https://www.nltk.org/book/
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L514
Applycation of NLP techniques learned throughout the course
CO4Understand (L2)TB1,TB2https://huggingface.co/learn/nlp-course
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L524PresentingfindingsCO4Apply (L3), Analyze (L4)TB1,TB2https://huggingface.co/learn/nlp-course
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L534Dialog system pipeline CO4Understand (L2), Apply (L3)TB1,TB2https://huggingface.co/learn/nlp-course
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L544System componentsCO4Apply (L3), Analyze (L4)TB2
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L554Revision TourCO4Apply (L3), Evaluate (L5)TB2freeCodeCamp.org
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L564Use case of NLPCO4Apply (L3), Analyze (L4)TB1,TB2What is NLP? | Codebasics
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L574NLP ResourcesCO1, CO2All LevelsTB1
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L584Hand-on ExcercisesCO3, CO4All LevelsTB3