NLP

Duration: 8 min

This video lesson is available to enrolled students.

Return to /learn/BIHAR-STET-COMPUTER-SCIENCE-COMPLETE-COURSE/unit-12-iot/emerging-trends/nlp-ar-vr/asset-nlp after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About the course: About the Course
  2. Mathematical Aptitude: संख्या पद्धति / Number System, महत्तम समापवर्तक और लघुत्तम समापवर्त्य / HCF and LCM, Divisibility Rules, प्रतिशत / Percentage, अनुपात और समानुपात / Ratio and Proportion, औसत / Average, लाभ, हानि और बट्टा / Profit, Loss and Discount, समय और कार्य / Time and Work, घन क्षेत्रमिति / Solid Mensuration, साधारण और चक्रवृद्धि ब्याज / Simple and Compound Interest, समय, चाल और दूरी / Time, Speed and Distance, Age Problems, मिश्रण और एलिगेशन / Mixture and Alligation, Algebra, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, Pipe and Cistern, Trigonometry, Statistics, आँकड़ा निर्वचन / Data Interpretation, Arithmetic, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, संख्या पद्धति और संख्या सिद्धांत / Number Systems and Number Theory, निर्देशांक ज्यामिति, सदिश और त्रिविमीय ज्यामिति / Coordinate Geometry, Vectors and 3D Geometry, बहुपद, समीकरण और सम्मिश्र संख्याएँ / Polynomials, Equations and Complex Numbers
  3. Logical Reasoning: श्रेणी / Series, कूट लेखन एवं कूट वाचन / Coding-Decoding, सादृश्यता एवं वर्गीकरण / Analogy and Classification, रक्त संबंध / Blood Relations, दिशा एवं दूरी / Direction and Distance, क्रम एवं रैंकिंग / Order and Ranking, अशाब्दिक तर्क / Non-Verbal Reasoning, बैठक व्यवस्था एवं पहेलियाँ / Seating Arrangement and Puzzles, Syllogisms, वेन आरेख, न्याय वाक्य एवं असमानता / Venn Diagrams, Syllogism and Inequalities, गणितीय संक्रियाएँ एवं लुप्त संख्या / Mathematical Operations and Missing Numbers, वर्णमाला एवं संख्या परीक्षण / Alphabet and Number Test, कथन आधारित तार्किक विवेचन / Statement-Based Reasoning, Analytical Reasoning (Counting Figures Reasoning), घन, पासा एवं आकृतियों की गिनती / Cubes, Dice and Figure Counting, कैलेंडर एवं घड़ी / Calendar and Clock, Clock
  4. General Knowledge: वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research, Chemistry, Biology, History, Geography, Polity, Indian Economy, समसामयिकी / Current Affairs, Static General Knowledge
  5. Art of Teaching: शिक्षण-अधिगम प्रक्रिया एवं कक्षा प्रबंधन / Teaching-Learning Process and Classroom Management, Learners & Learning Process, Factors Affecting Teaching, शिक्षण विधियाँ एवं उपागम / Teaching Methods and Approaches, सूक्ष्म शिक्षण एवं कक्षा संप्रेषण / Microteaching and Classroom Communication, शिक्षण सहायक सामग्री एवं शैक्षिक तकनीकी / Teaching Aids and Educational Technology, आकलन एवं मूल्यांकन / Assessment and Evaluation, शिक्षक: गुण, व्यावसायिक नैतिकता एवं विकास / Teacher: Qualities, Professional Ethics and Development, शैक्षिक नीतियाँ, अधिनियम एवं प्रशासन / Educational Policies, Acts and Administration, पाठ्यचर्या, कला एवं सह-शैक्षिक क्षेत्र / Curriculum, Arts and Co-curricular Areas, निर्देशन एवं परामर्श / Guidance and Counselling, दिव्यांग बच्चों की शिक्षा / Education of Children with Disabilities, Micro-Teaching & Teaching Skills, पाठ योजना / Lesson Planning
  6. Environmental Science: Environment
  7. Unit 1 | Digital Electronics: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  8. Unit 2 | COA: Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Programming the Basic Computer
  9. Unit 3 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  10. Unit 3 | Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. Unit 4 | Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  12. Unit 5 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management
  13. Unit 6 | Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Transaction Management, Concurrency Control
  14. Unit 7 | Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer
  15. Unit 8 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  16. Unit 9 | Object Oriented Programming: OOPS with C++
  17. Unit 10 | Web Development: HTML Basics, CSS, JavaScript-Basics, XML, Node.js
  18. Unit 11 | Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines
  19. Unit 12 | IoT: Emerging Tech Trends, Cloud Computing, कंप्यूटर नेटवर्क / Computer Networks
  20. Unit 13 | Artificial Intelligence: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms
  21. Unit 14 | E-Commerce: Electronic Data Interchange and E-Commerce, Role of Internet and Web Services, Security in Mobile and Wireless Computing, Physical Security and Cryptography Basics, Cyber Safety and Security
  22. Unit 15 | Multimedia: Multimedia
  23. Unit 16 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  24. Full Length Mock Tests — General Paper:
  25. Full Length Mock Tests — Computer Science (Paper II):
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video provides a structured introduction to Natural Language Processing (NLP), a critical field within Artificial Intelligence. The lecture begins by defining NLP and its core objective of bridging the gap between human communication and computer logic. It then details the technical workflow of NLP systems, covering eight distinct steps from data collection to model iteration. Finally, the session concludes by evaluating the practical advantages, such as automation and scale, alongside significant limitations like ambiguity and lack of emotional intelligence. This comprehensive overview equips students with a foundational understanding of how NLP functions and its real-world applications, supported by clear visual aids and concrete examples.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor introduces NLP using a slide titled 'Natural Language Processing (NLP)'. He defines it as a branch of AI that gives computers the ability to 'understand, interpret, and manipulate human language (text and speech) just like humans do'. He highlights the core objective to 'bridge the gap between human communication (which is complex and ambiguous) and computer understanding (which requires precise logic)'. Key components like 'Natural Language Understanding (NLU)' and 'Natural Language Generation (NLG)' are listed with examples such as 'Book a flight' being a command, not a discussion about books. He also traces the evolution from 1950s machine translation to today's advanced AI assistants. The instructor underlines key phrases on the slide to emphasize the definition and the distinction between human and computer logic.

  2. 2:00 – 5:00 02:00-05:00

    The lecture transitions to the 'Working of Natural Language Processing', detailing an 8-step process. Step 1 is 'Text Input & Data Collection', exemplified by 'Downloading 10,000 movie reviews to train a sentiment analysis system'. Step 2 involves 'Text Preprocessing' like removing noise, stop words, and stemming 'running', 'ran', and 'runs' to the single root word 'run'. Step 3 is 'Text Representation' converting text into numbers (Vectors) using techniques like 'Bag of Words'. The instructor explains 'Feature Selection' to identify relevant words while ignoring irrelevant details like dates. He discusses 'Model Selection & Training' choosing algorithms like Neural Networks. Finally, 'Model Deployment & Inference' is shown with a banking chatbot example, followed by 'Iteration & Improvements' where the model learns new slang like 'Lit' or 'Bet'. The slide visually maps these steps in a circular diagram on the right side.

  3. 5:00 – 8:19 05:00-08:19

    The final section covers 'Advantages and Limitations of Natural Language Processing (NLP)'. Advantages include 'Enables Natural Communication' allowing interaction in English or Hindi, 'Efficiency at Scale' for analyzing thousands of reviews instantly, and 'Saves Time & Automates Tasks' like Google Translate. '24/7 User Support' is highlighted with banking bots helping at midnight. Limitations listed are 'Ambiguity in Language' where words like 'Bank' can mean a financial institution or a river side. 'Lack of Emotional Intelligence' makes systems struggle to detect sarcasm, such as classifying 'Great job!' said angrily as positive. 'Data Quality Dependency' notes that slang or typos confuse the model, and 'Complex Sentence Structures' like legal documents are often misinterpreted. The instructor uses red arrows to point to each advantage and limitation on the slide.

The video provides a comprehensive overview of NLP, starting with its fundamental definition as an AI branch for human-computer language interaction. It then systematically breaks down the technical workflow, from data collection and preprocessing to model training and deployment. Finally, it contextualizes the technology by weighing its practical benefits, such as automation and scale, against inherent challenges like ambiguity and emotional intelligence gaps. This progression moves from theoretical definition to practical implementation and critical evaluation, offering a complete picture of the technology's capabilities and constraints. The instructor effectively uses slide annotations and diagrams to reinforce these concepts throughout the lecture.

Loading lesson…