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2 | COURSE PLAN (AY 26-27) | |||||||||||||||||||||||
3 | AS PER BCA SCHEME & SYLLABUS 2024-2025 ONWARDS GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY, NEW DELHI | |||||||||||||||||||||||
4 | Session - 2026-27 | |||||||||||||||||||||||
5 | Programme Code | Course Code | Course Name | Programme | Course Type | Semester | Department | |||||||||||||||||
6 | 020 | BCA305T | Natural Language Processing. | BCA | CCT | Fifth | DICT | |||||||||||||||||
7 | Contact Hours Per Week | Total Credits | Assessment Marks | |||||||||||||||||||||
8 | L | T | P | Internal (CIA) | External | |||||||||||||||||||
9 | 4 | - | - | 4 | 40 | 60 | ||||||||||||||||||
10 | Contact Hours Per Semester | |||||||||||||||||||||||
11 | L | T | P | |||||||||||||||||||||
12 | 4 | 0 | 0 | |||||||||||||||||||||
13 | 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 | |||||||||||||||||||||||
14 | 2. Course Outcome & Mapping, Course Articulation | |||||||||||||||||||||||
15 | Course Outcome | PO1 | PO2 | PO3 | PO5 | PO6 | PO7 | PO9 | PO10 | |||||||||||||||
16 | CO305T.1 : Understand NLP fundamentals, Python-based text processing, and its applications. | 3 | 1 | - | 4 | - | 2 | - | 2 | |||||||||||||||
17 | CO305T.2: Apply text preprocessing, corpora access, representation methods | 2 | 2 | 2 | 3 | - | 1 | - | 2 | |||||||||||||||
18 | CO305T.3: Exploring language modelling techniques | 3 | 3 | 3 | 2 | - | 2 | - | 3 | |||||||||||||||
19 | CO305T.4: Acquire skills in tagging, classification, sentiment analysis, and apply to real-world challenges. | 3 | 2 | 1 | 2 | 2 | 2 | - | 3 | |||||||||||||||
20 | Course Articulation (Average) | |||||||||||||||||||||||
21 | 1 - (Low); 2 - (Moderate); 3 – (High) | |||||||||||||||||||||||
22 | 3. Pre-Requisites | |||||||||||||||||||||||
23 | 1 | Python Programming Language | ||||||||||||||||||||||
24 | ||||||||||||||||||||||||
25 | 4. Pedagogical approaches (aligned with Teaching Learning Strategies): | |||||||||||||||||||||||
26 | S.N | Teaching-Learning Strategy | Aligned Pedagogical Approach | |||||||||||||||||||||
27 | 1 | Technology Based Learning | Flipped Classroom | |||||||||||||||||||||
28 | 2 | Learning through Problem-Solving | Inquiry-Based Learning | |||||||||||||||||||||
29 | 3 | Project Based Learning | Collaborative Learning | |||||||||||||||||||||
30 | 5. Pedagogy used | |||||||||||||||||||||||
31 | S.N | Pedagogy used | ||||||||||||||||||||||
32 | 1 | Business Simulations, Role Plays, Gamified Quizzes, Scenario-Based Activities, Management Games | ||||||||||||||||||||||
33 | 2 | Peer Presentations, Student Seminars, Peer Evaluation | ||||||||||||||||||||||
34 | 3 | Group Discussions, Team Assignments, Brainstorming Sessions, Collaborative Projects, Case Discussions | ||||||||||||||||||||||
35 | 6. ICT Tools Used | |||||||||||||||||||||||
36 | S.N | ICT Tools Used | ||||||||||||||||||||||
37 | 1 | Laptops/Projector (for lectures and demonstrations),Python (with libraries like NumPy, SciPy, Scikit-learn, NLTK) (for practical implementation). | ||||||||||||||||||||||
38 | 2 | Voyant Tools | ||||||||||||||||||||||
39 | 3 | Online Tools & Use of Virtual Lab | ||||||||||||||||||||||
40 | 4 | LMS (Moodle/Google Classroom) for assignments and assessments | ||||||||||||||||||||||
41 | 7. Lesson Plan | |||||||||||||||||||||||
42 | Lecture No. | Unit No. | Topic | Mapping with CO | BT Level | References | Other resources:Web link/Videos Links/You Tube links(Wherever Applicable) | |||||||||||||||||
43 | L1 | 1 | Introduction to NLP | CO1 | Remember (L1), Understand (L2) | TB1 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
44 | L2 | 1 | Overview of Natural Language Processing | CO1 | Understand (L2) | TB1 | https://www.nltk.org/book/ | |||||||||||||||||
45 | L3 | 1 | History and evolution of NLP | CO1 | Understand (L2), Apply (L3) | TB1,TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
46 | L4 | 1 | Applications of NLP in real-world scenarios | CO1 | Apply (L3), Analyze (L4) | TB2 | freeCodeCamp.org | |||||||||||||||||
47 | L5 | 1 | Approaches to NLP | CO1 | Apply (L3), Analyze (L4) | TB2 | Approaches to NLP Videos | |||||||||||||||||
48 | L6 | 1 | Computing with Language | CO1 | Apply (L3), Analyze (L4) | TB2 | What is NLP? | Codebasics | |||||||||||||||||
49 | L7 | 1 | Texts and Words | CO1 | Understand (L2), Analyze (L4) | TB1,TB2 | Hugging Face – The AI community building the future. | |||||||||||||||||
50 | L8 | 1 | A Closer Look at Python | CO1 | Analyze (L4) | TB1,TB2 | NLP Videos | |||||||||||||||||
51 | L9 | 1 | Texts as Lists of Words | CO1 | Apply (L3) | TB1,TB2 | Natural language processing Videos | |||||||||||||||||
52 | L10 | 1 | dictionaries | CO1 | Apply (L3), Analyze (L4) | TB1,TB2 | AI CH AI464 UV 16×9 | |||||||||||||||||
53 | L11 | 1 | Ethical considerations and bias in NLP | CO1 | Apply (L3), Analyze (L4) | TB1,TB2 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
54 | L12 | 1 | Current research trends and emerging applications in NLP | CO1 | Analyze (L4) | TB1,TB2 | ||||||||||||||||||
55 | L13 | 1 | Introduction to corpora | CO1 | Apply (L3), Analyze (L4) | TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
56 | L14 | 2 | Introduction to text data | CO1 | Apply (L3), Analyze (L4) | TB2 | ||||||||||||||||||
57 | L15 | 2 | Accessing Text Corpora | CO2 | Remember (L1), Understand (L2) | TB1,TB2 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
58 | L16 | 2 | Accessing Text Lexical Resources | CO2 | Understand (L2) | TB1 | https://www.nltk.org/book/ | |||||||||||||||||
59 | L17 | 2 | Conditional Frequency | CO2 | Analyze (L4) | TB1 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
60 | L18 | 2 | Lexical Resources | CO2 | Understand (L2), Apply (L3) | TB1 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
61 | L19 | 2 | WordNetLevel, | CO2 | Understand (L2), Apply (L3) | TB1,TB2 | https://www.nltk.org/book/ | |||||||||||||||||
62 | L20 | 2 | NLP Pipeline | CO2 | Understand (L2), Apply (L3) | TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
63 | L21 | 2 | Strings | CO2 | Apply (L3), Analyze (L4) | TB2 | ||||||||||||||||||
64 | L22 | 2 | Text Processing at the Lowest | CO2 | Apply (L3), Analyze (L4) | TB2 | Approaches to NLP Videos | |||||||||||||||||
65 | L23 | 2 | Text preprocessing techniques | CO2 | Analyze (L4) | TB1,TB2 | What is NLP? | Codebasics | |||||||||||||||||
66 | L24 | 2 | Tokenization | CO2 | Apply (L3), Analyze (L4) | TB1,TB2 | Hugging Face – The AI community building the future. | |||||||||||||||||
67 | L25 | 2 | Stemming | CO2 | Apply (L3), Analyze (L4) | TB1,TB2 | ||||||||||||||||||
68 | L26 | 2 | Lemmatization | CO2 | Analyze (L4) | TB1,TB2 | Natural language processing Videos | |||||||||||||||||
69 | L27 | 3 | Language Modeling | CO2 | Apply (L3), Analyze (L4) | TB1,TB2 | AI CH AI464 UV 16×9 | |||||||||||||||||
70 | L28 | 3 | Probability theory and | CO3 | Understand (L2) | TB1,TB2 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
71 | L29 | 3 | Statistical language models, | CO3 | Understand (L2), Apply (L3) | TB2 | https://www.nltk.org/book/ | |||||||||||||||||
72 | L30 | 3 | Syntactic analysis | CO3 | Apply (L3), Analyze (L4) | TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
73 | L31 | 3 | parsing techniques | CO3 | Apply (L3), Analyze (L4) | TB1,TB2 | freeCodeCamp.org | |||||||||||||||||
74 | L32 | 3 | Vector space models | CO3 | Apply (L3), Analyze (L4) | TB1 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
75 | L33 | 3 | One-hot encoding | CO3 | Analyze (L4) | TB1 | https://www.nltk.org/book/ | |||||||||||||||||
76 | L34 | 3 | Bag-of-Words (BoW) mode | CO3 | Understand (L2) | TB1 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
77 | L35 | 3 | l, TF-IDF (Term Frequency-Inverse Document Frequency) representation | CO3 | Apply (L3), Analyze (L4) | TB1,TB2 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
78 | L36 | 3 | n-gram models | CO3 | Apply (L3), Evaluate (L5) | TB2 | https://www.nltk.org/book/ | |||||||||||||||||
79 | L37 | 3 | Training sets | CO3 | Understand (L2) | TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
80 | L38 | 3 | Test sets | CO3 | Apply (L3), Analyze (L4) | TB2 | ||||||||||||||||||
81 | L39 | 3 | Evaluating | CO3 | Analyze (L4) | TB1,TB2 | Approaches to NLP Videos | |||||||||||||||||
82 | L40 | 3 | Smoothing | CO3 | Analyze (L4) | TB1,TB2 | What is NLP? | Codebasics | |||||||||||||||||
83 | L41 | 3 | NLP Tasks and Techniques | CO3 | Apply (L3), Analyze (L4) | TB1,TB2 | Hugging Face – The AI community building the future. | |||||||||||||||||
84 | L42 | 4 | NLP Techniques | CO4 | Remember (L1), Understand (L2) | TB1,TB2 | NLP Videos | |||||||||||||||||
85 | L43 | 4 | Part-of-speech tagging | CO4 | Understand (L2) | TB1,TB2 | Natural language processing Videos | |||||||||||||||||
86 | L44 | 4 | Named Entity Recognition (NER) | CO4 | Understand (L2), Apply (L3) | TB1,TB2 | AI CH AI464 UV 16×9 | |||||||||||||||||
87 | L45 | 4 | Sentiment analysis | CO4 | Apply (L3), Analyze (L4) | TB2 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
88 | L46 | 4 | Supervised Text Classification | CO4 | Apply (L3), Analyze (L4) | TB2 | https://www.nltk.org/book/ | |||||||||||||||||
89 | L47 | 4 | Evaluation | CO4 | Analyze (L4) | TB1,TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
90 | L48 | 4 | Naïve Bayes Classifiers | CO4 | Analyze (L4) | TB1 | freeCodeCamp.org | |||||||||||||||||
91 | L49 | 4 | Chatbot | CO4 | Understand (L2), Apply (L3) | TB1,TB2 | https://web.stanford.edu/~jurafsky/slp3/ | |||||||||||||||||
92 | L50 | 4 | Case study: Students will work on a real-world NLP problem | CO4 | Apply (L3), Analyze (L4) | TB1,TB2 | https://www.nltk.org/book/ | |||||||||||||||||
93 | L51 | 4 | Applycation of NLP techniques learned throughout the course | CO4 | Understand (L2) | TB1,TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
94 | L52 | 4 | Presentingfindings | CO4 | Apply (L3), Analyze (L4) | TB1,TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
95 | L53 | 4 | Dialog system pipeline | CO4 | Understand (L2), Apply (L3) | TB1,TB2 | https://huggingface.co/learn/nlp-course | |||||||||||||||||
96 | L54 | 4 | System components | CO4 | Apply (L3), Analyze (L4) | TB2 | ||||||||||||||||||
97 | L55 | 4 | Revision Tour | CO4 | Apply (L3), Evaluate (L5) | TB2 | freeCodeCamp.org | |||||||||||||||||
98 | L56 | 4 | Use case of NLP | CO4 | Apply (L3), Analyze (L4) | TB1,TB2 | What is NLP? | Codebasics | |||||||||||||||||
99 | L57 | 4 | NLP Resources | CO1, CO2 | All Levels | TB1 | ||||||||||||||||||
100 | L58 | 4 | Hand-on Excercises | CO3, CO4 | All Levels | TB3 | ||||||||||||||||||