{"id":29440,"date":"2025-04-14T17:51:12","date_gmt":"2025-04-14T12:21:12","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/315329-natural-language-processing-syllabus-for-data-science-5th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T17:51:12","modified_gmt":"2025-04-14T12:21:12","slug":"315329-natural-language-processing-syllabus-for-data-science-5th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/315329-natural-language-processing-syllabus-for-data-science-5th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"315329: Natural Language Processing Syllabus for Data Science 5th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Natural Language Processing detailed Syllabus for Data Science (DS), K scheme PDF has been taken from the MSBTE<\/a> official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical\/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below. <\/p>\n

For all other MSBTE Data Science 5th Sem K Scheme Syllabus PDF, do visit MSBTE Data Science 5th Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for natural language processing is as follows.<\/p>\n

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

    \n
  1. Explain key concepts linguistics and NLP.<\/li>\n
  2. Implement Text Normalization and Text Preprocessing techniques to the text.<\/li>\n
  3. Apply Part Of Speech ,Parsing ,Named Entity Recognition techniques to the text.<\/li>\n
  4. Generate text embedding in NLP.<\/li>\n
  5. Use Transformer in NLP applications.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Natural Language Basics 1.1\tOverview of NLP ,The need of NLP, Areas of study under linguistics 1.2\tLanguage Syntax and Structure: Words, Phrases, Clauses, Grammar, Word Order Typology, Word Order-Based Language Classification 1.3\tLanguage Semantics: Lexical Semantic Relations,Semantic Networks and Models 1.4\tText Corpora:Corpora Annotation and Utilities, Popular Corpora 1.5\tApplications of NLP.\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Presentations\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Text Syntax and Structure 3.1\tPart-of-speech tagging : English Word Classes ,Part-of-Speech Tagging 3.2\tNamed entity recognition :Named Entities and Named Entity Tagging ,IOB\/ BIO tagging 3.3\tParsing Techniques :Partial parsing\/chunking ,Dependency parsing\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Demonstration Hands-on\n<\/p>\n

    Unit IV<\/h4>\n

    Text Feature Extraction 4.1\tVector Space Models :Words and Vectors , Cosine for measuring similarity 4.2\tOne hot encoding ,Bag-of-words ,TF-IDF 4.3\tWord2vec:continuous bag of words, skip gram 4.4\tContextual Embeddings : Contextual Embeddings ,Word Sense\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Demonstration Hands-on\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. Implement a program to use text corpus. i)Brown corpus, ii)Penn Treebank Corpus. 2<\/li>\n
    2. i)Write program for Sentence Segmentation Techniques. ii)Write program Word Segmentation using Re and nltk. 2<\/li>\n
    3. Implement Penn Treebank tokenization, word_tokenize,wordpunct_tokenize,sent_tokenize,WhitespaceTokenizer. 2<\/li>\n
    4. *Apply various Lemmatization Techniques and Stemming Techniques such as porter stemmer,Lancaster Stemmer,Stemmer on text. 2<\/li>\n
    5. *Write program on Text Normalization using nltk: i)Tokenizing text ii)Removing special charactersiii)contractions iv)case conversion. ii)*Generate unigram, bigram, trigram for given text. 2<\/li>\n
    6. *Write program for POS tagging on the given text. 2<\/li>\n
    7. *Write program to find Named Entity Recognition(NER) for the given text . 2<\/li>\n
    8. i)Implement a program for dependency parse tree on the sentence using nltk or spacy. ii)Write program performing chunking on the given text .Extract Noun Phrases, Verb Phrases, Adjective Phrases. 2<\/li>\n
    9. *Write program to generate word embedding using word2Vec and BERT embedding(use Hugging Face). 2<\/li>\n
    10. Perform the prediction task using NLP and ML classifiers: a)Sentiment analysis b) Fake news detection. 2<\/li>\n
    11. * Implement program to fine-tune a pre-trained model from Hugging Face’s for text classification. 2<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Micro Project<\/i><\/p>\n

        \n
      1. Sentiment Analysis – Develop a model to classify text as positive, negative, or neutral using NLP techniques. 2)Fake News Detection – Train a classifier to differentiate between real and fake news articles based on linguistic patterns. 3)Keyword Extraction – Extract the most relevant keywords from a document using NLP algorithms like TF-IDF or RAKE.<\/li>\n<\/ol>\n

        Laboratory Equipment<\/h4>\n

        For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
        It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
        \"Get<\/a>.<\/p>\n

        Learning Materials<\/h4>\n
          \n
        1. Daniel Jurafsky\tSpeech and Language Processing -ch2 2.1,2.3,2.4 ch3,ch4\tPearson Publication ISBN :978-0131873216<\/li>\n
        2. Dipanjan Sarkar\tText Analytics with Python ch1 ch5 5.1\tApress ISBN-13 (pbk): 978-1-4842-2387-1<\/li>\n
        3. Steven Bird, Ewan Klein, and Edward Loper\tNatural Language Processing with python ch5 5.2 5.3 5.4\tOreally ISBN:978-0-596-51649-9<\/li>\n
        4. Akshay Kulkarni Adarsha Shivananda\tNatural Language Processing Recipes_ Unlocking Text Data with Machine Learning and Deep Learning using Python . for lab 1 to 11\tApress ISBN-13 (pbk): 978-1-4842-4266-7<\/li>\n
        5. Pushpak Bhattacharyya and Aditya Joshi\tNatural Language Processing ch2-2.5 ch3-3.3\tWilley ISBN:978-93-5746-238-9<\/li>\n<\/ol>\n

          Learning Websites<\/h4>\n
            \n
          1. https:\/\/web.stanford.edu\/~jurafsky\/slp3\/\t\tNLP e-book and PPT<\/li>\n
          2. https:\/\/github.com\/Donges-Niklas\/Intro-to-NLP-with-NLTK\/blob \/master\/nltk.ipynb\t\tText Segmentation, Stop Words & Word Segmentation, Stemming ,Parsing (Speech Tagging & Chunking),programs<\/li>\n
          3. https:\/\/github.com\/samiramunir\/Simple-Sentiment-Analysis-usi ng-NLTK\/blob\/master\/live_classifier.py\t\tsentiment Analysis Program<\/li>\n
          4. https:\/\/www.youtube.com\/watch? v=yLDRHyNJSXA&list=PLPIwNooIb9 vimsumdWeKF3BRzs9tJ-_gy&index=38\t\tSentiment Analysis theory content<\/li>\n
          5. https:\/\/www.youtube.com\/watch? v=fM4qTMfCoak&list=PLZoTAELRMX VMdJ5sqbCK2LiM0HhQVWNzm\t\tNLP concept playlist<\/li>\n<\/li>\n<\/ol>\n

            For detail Syllabus of all other subjects of Data Science, K scheme do visit Data Science 5th Sem Syllabus for K scheme<\/a>.<\/p>\n

            For all Data Science results, visit MSBTE Data Science all semester results<\/a> direct links.<\/p>\n","protected":false},"excerpt":{"rendered":"

            Natural Language Processing detailed Syllabus for Data Science (DS), K scheme PDF has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject […]<\/p>\n","protected":false},"author":2351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[119,141],"tags":[],"class_list":["post-29440","post","type-post","status-publish","format-standard","hentry","category-5th-sem-msbte","category-ds"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts\/29440","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/users\/2351"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/comments?post=29440"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts\/29440\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/media?parent=29440"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/categories?post=29440"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/tags?post=29440"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}