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For detailed Syllabus of all other subjects of AI&ML AN 5th Sem, K scheme do visit Ai&Ml 5th Sem Syllabus for K scheme.

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For the results of Diploma 5th Sem, kindly visit MSBTE Diploma 5th Sem direct result link.

For the academic calendar of Diploma 5th Sem, kindly visit MSBTE Diploma 5th Sem academic calendar.

Wishing you great luck ahead.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for natural language processing is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

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

Unit I

Natural Language Basics 1.1 Overview of NLP ,The need of NLP, Areas of study under linguistics 1.2 Language Syntax and Structure: Words, Phrases, Clauses, Grammar, Word Order Typology, Word Order-Based Language Classification 1.3 Language Semantics: Lexical Semantic Relations,Semantic Networks and Models 1.4 Text Corpora:Corpora Annotation and Utilities, Popular Corpora 1.5 Applications of NLP.

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Unit III

Text Syntax and Structure 3.1 Part-of-speech tagging : English Word Classes ,Part-of-Speech Tagging 3.2 Named entity recognition :Named Entities and Named Entity Tagging ,IOB/ BIO tagging 3.3 Parsing Techniques :Partial parsing/chunking ,Dependency parsing

Suggested Learning Pedagogie
Lecture Using Chalk-Board Demonstration Hands-on

Unit IV

Text Feature Extraction 4.1 Vector Space Models :Words and Vectors , Cosine for measuring similarity 4.2 One hot encoding ,Bag-of-words ,TF-IDF 4.3 Word2vec:continuous bag of words, skip gram 4.4 Contextual Embeddings : Contextual Embeddings ,Word Sense

Suggested Learning Pedagogie
Lecture Using Chalk-Board Demonstration Hands-on

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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List of Experiments:

1. Implement a program to use text corpus. i)Brown corpus, ii)Penn Treebank Corpus. 2
2. i)Write program for Sentence Segmentation Techniques. ii)Write program Word Segmentation using Re and nltk. 2
3. Implement Penn Treebank tokenization, word_tokenize,wordpunct_tokenize,sent_tokenize,WhitespaceTokenizer. 2
4. *Apply various Lemmatization Techniques and Stemming Techniques such as porter stemmer,Lancaster Stemmer,Stemmer on text. 2
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
6. *Write program for POS tagging on the given text. 2
7. *Write program to find Named Entity Recognition(NER) for the given text . 2
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
9. *Write program to generate word embedding using word2Vec and BERT embedding(use Hugging Face). 2
10. Perform the prediction task using NLP and ML classifiers: a)Sentiment analysis b) Fake news detection. 2
11. * Implement program to fine-tune a pre-trained model from Hugging Face’s for text classification. 2

Self Learning

Micro Project

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.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
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Learning Materials

1. Daniel Jurafsky Speech and Language Processing -ch2 2.1,2.3,2.4 ch3,ch4 Pearson Publication ISBN :978-0131873216
2. Dipanjan Sarkar Text Analytics with Python ch1 ch5 5.1 Apress ISBN-13 (pbk): 978-1-4842-2387-1
3. Steven Bird, Ewan Klein, and Edward Loper Natural Language Processing with python ch5 5.2 5.3 5.4 Oreally ISBN:978-0-596-51649-9
4. Akshay Kulkarni Adarsha Shivananda Natural Language Processing Recipes_ Unlocking Text Data with Machine Learning and Deep Learning using Python . for lab 1 to 11 Apress ISBN-13 (pbk): 978-1-4842-4266-7
5. Pushpak Bhattacharyya and Aditya Joshi Natural Language Processing ch2-2.5 ch3-3.3 Willey ISBN:978-93-5746-238-9

Learning Websites

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

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for cloud computing for data science is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Use Cloud-Based software services by comprehending the cloud Computing Architecture.
2. Configure Virtual Machines using Virtualization techniques.
3. Implement Virtualized storage system in Cloud.
4. Use Machine Learning algorithms in Cloud Environment.
5. Deploy Machine Learning Models on Cloud.

Unit I

Introduction to Cloud Computing 1.1 Introduction to Cloud Computing: Definition, Evolution of Cloud computing (from Mainframes to Clouds), Service – Oriented Architecture, Web Services, Grid Computing, Utility Computing 1.2 Characteristics of a Cloud computing 1.3 Cloud computing architecture: Basic components: front-end platform, back-end, platform, Networking, cloudbased delivery 1.4 Cloud Service Models: Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Continuous delivery using PaaS

Suggested Learning Pedagogie
Lecture Using Chalk-Board Flipped Classroom Presentations

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Unit III

Cloud Storage 3.1 Cloud Storage: Introduction, Benefits of using Cloud Storage, Use cases of Cloud Storage (Backup, Archiving, Disaster recovery, Data processing, Content delivery) 3.2 Cloud storage system: Block-Based, File-Based, Object-Based Storages 3.3 Key-Value Databases: Introduction, features, limitations 3.4 Batch data and Streaming data in Machine learning 3.5 Cloud Data Warehouse- AWS Redshift 3.6 Various Cloud-based tools used for data science in ML- GCP BigQuery

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit IV

Cloud Computing for Data Science 4.1 Machine Learning in the Cloud: Benefits and Limitations 4.2 Types of Cloud-Based Machine Learning Services: Artificial Intelligence as a Service (AIaaS), GPU as a Service (GPUaaS) 4.3 Introduction to various ML systems and benefits of using Managed ML platforms 4.4 Cloud Machine Learning Platforms: AWS SageMaker, Azure Machine Learning studio, Google Cloud AutoML

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

List of Experiments:

1. * Use Google Services to create Doc/Sheet/Keep/Forms 2
2. *Create Virtual Machine using VMware workstation for Windows/Linux 2
3. Create Web server (WAMP/XAMP/APACHE) on Virtual Machine 2
4. *Create an account on AWS or Azure, or Google Cloud Platform 2
5. Create an account on Google Cloud Platform. a) Create a project and access BigQuery. b) Query data directly from Google Sheets. c) Create tables and views using BigQuery 2
6. Launch an EC2 instance with a specified configuration and configure Block-Based storage 2
7. *Create and Configure File-Based storage on EC2 instance 2
8. Create and Configure Object-Based storage on EC2 instance 2
9. Write a script in Python to upload a dataset to an S3 bucket, list the files in the bucket, and download a file 2
10. *Create instance of Amazon Sagemaker notebook 2
11. * Build, test, tune, train, deploy and validate a model using Amazon Sagemaker

Self Learning

Micro Project

• Use AWS Glue, Google Dataflow, or Azure Data Factory to create a data pipeline that ingests, transforms, and loads data from one storage service to another. Document the pipeline configuration and execution results.
• Create a Machine on AWS and make basic services available on machine like word, power point etc.
• Set up a relational database using AWS RDS, Azure SQL Database, or Google Cloud SQL. Create a database schema, insert sample data, and perform queries using SQL. Document the steps and results.
• Launch an EC2 instance with a specified configuration (e.g., type, region). Install software (e.g., Python, Jupyter Notebook) on the instance. Connect to the instance and run a basic Python script.
• Create a local cloud on Virtual Machine on VmWare workstation. Deploy a web application and make it accessible by URL.
• Write and deploy a simple AWS Lambda function or Azure Function that processes data (e.g., transforms a JSON file). Test the function with sample data and set up triggers for automatic execution.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Materials

1. Dr. Anand Nayyar Handbook of Cloud Computing BPB Publication First Edition (1 January 2019) ISBN-10:9388176669 ISBN-13:978-9388176668
2. Toby Velte, Anthony Velte, Robert C Cloud Computing: A Practical Approach By Toby Velte, Anthony Velte, Robert C McGraw Hill Professional ISBN-978-007-162965-8
3. Noah Gift, Alfredo Deza Cloud Computing for Data Analysis Pragmatic AI Labs (No ISBN)
4. Valliappa Lakshmanan Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines O’Reilly Media, Inc. ISBN: 9781098118952
5. Abhishek Mishra Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker Wiley Publication ISBN: 978-1-11955671-8

Learning Websites

1. https://www.geeksforgeeks.org/virtualization-cloud computin g-types/ – Introduction to virtualization and cloud Computing and its types
2. https://www.javatpoint.com/virtualization-in-cloud-computing Introduction to virtualization and cloud Computing
3. https://www.hostitsmart.com/blog/types-of-virtualization-in- cloud-computing-complete-overview/ Overview of cloud computing, types of Virtualization advantages and Application
4. https://aws.amazon.com/what-is/virtualization/ How can AWS help with virtualization and cloud computing?
5. https://www.run.ai/guides/machine-learning-in-the-cloud Machine Learning in the Cloud,AWS Sagemaker Service for Machine Learning

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for advance database management is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Apply the concept of concurrency control.
2. Analyse various database architectures
3. Use Object Oriented and XML queries on Database.
4. Manipulate data using NoSQL commands.
5. Use data mining and warehousing concepts.

Unit I

Database System Architecture 1.1 Concurrency Control Techniques: Concurrency control protocols: Locked Based protocols, granting of locks, Two Phase Locking protocol 1.2 Database Model: Centralized Database System, Server System Architecture, Transaction Server, Data Server

Suggested Learning Pedagogie
Video Demonstrations Presentations Lecture Using Chalk-Board

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Unit III

Object Based Database & XML 3.1 Object Based Database: Overview, Complex data types, Structured types and inheritance in SQL 3.2 Table inheritance 3.3 Array and multiset types in SQL 3.4 Object-oriented vs. Object-Relational database 3.5 XML: Introduction, Structure of Xml Data, Xml Document Schema, Xpath, XQuery: FLWOR Expressions, Joins, Nested Queries, Sorting of Functions, Functions and Types

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Video Demonstrations

Unit IV

NoSQL & MongoDB 4.1 Structured versus Unstructured Data 4.2 NoSQL database concepts: Types of NoSQL database, NoSQL data modeling, Benefits of NoSQL, comparison between SQL and NoSQL database system 4.3 NoSQL using MongoDB: Introduction to MongoDB Shell, Running the MongoDB shell, MongoDB client, Basic operations with MongoDB shell, Basic Data Types ,Arrays, Embedded Documents 4.4 Querying with MongoDB: find() function, specifying which keys to return, query criteria, OR queries, Types specific querying

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Hands-on

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

List of Experiments:

1. Install suitable Database. 2 .Configure a server-based database and establish client-server connections. 2
2. *Execute query to implement Locked Based protocols. 2
3. Study Parallel and Distributed system using Case. 2
4. Create database using XML 1. Create a xml file for given Application 2. Create database using xml file 3. Confirm database path 4.Show database 2
5. *4.1 Implement queries based on FLWOR expressions 1. Create a xml file 2. Confirm the path expression 3. Use FLWOR expression for given criteria to display result from xml file 4. Execute Join queries *4.2 Implement queries based on nested queries and sorting of results using XQuery 1. Create a xml file 2. Execute queries based on Nested queries and sorting of results using XQuery 2
6. *Execute query using type inheritance and table inheritance 1. Create Parent Table and child table for given application 2. Execute queries using inheritance approach by combining a data from parent, child tables 2
7. *Execute query using Array and Multiset types in SQL 1. Create an array Type and Multiset type 2. Use array type and Multiset type as a column name in table 3. Insert and display the data from table 2
8. *Execute MongoDB Query using basic operations 1. Create a database for given application 2. Use DATABASE statement 3. Insert, update and delete the record for given application 2
9. *9.1 : Implement aggregation queries 1. Write MongoDB queries using aggregate function for given application *9.2: Execute query using find() function 1. Write MongoDB queries using find () for given application 2
10. *Use Data warehousing tool (ETL) 1. Extract the relevant data from the source database 2. Transform the data so that it is better suited for analytics 3. Load the data into the target database 2
11. Implement Classification Techniques in Data Mining 2

Self Learning

Micro Project

• Develop and maintain XML database for Employee Attendance System
• Develop a MongoDB database for tracking patient history in a healthcare system.
• Develop a MongoDB database for tracking issued and pending books in a library.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Materials

1. Korth Henery Database System Concepts McGraw Hill Education, New Delhi, 6th Edition ,ISBN -13:978-93-329-0138-4
2. Chakrabarti, Dasgupta, Shinde, KLSI Advanced Database Management System Dreamtech Press ,ISBN 13 :9789351194552
3. Bayross Ivan SQL, PL/SQL The Programming Language of ORACLE BPB Publications, New Delhi, 3rd Edition ISBN-13: 978-8176569644
4. Jiawei Han,Micheline Kamber,Jian Pei Data Mining Concepts and Techniques Morgan Kaufmann ,USA,3rd Edition, ISBN-978-0-12-381479-1

Learning Websites

1. https://docs.mongodb.com/manual/tutorial/install-mongodb-on- windows/ MangoDB installation
2. www.learn-with-video-tutorials.com/data-warehouse-tutorial-v ideo Advanced database management system concept
3. https://www.javatpoint.com/xml-database XML Tutorial
4. https://www.javatpoint.com/data-warehouse Data Warehouse and Data Mining
5. https://www.youtube.com/watch? v=L54ajG7vtZA&list=PLPphbOQYOr DrTLR_4BBxYpaJAtluFEkS9 ADVANCED DATABASE CONCEPTS-(DATABASE SYSTEM ARCHITECTURES)

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for internship is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Observe time/resource management and industrial safety aspects.
2. Acquire professional experience of industry environment .
3. Establish effective communication in working environment.
4. Prepare report of assigned activities and accomplishments.

General Guidlines

1. Duration of Training – 12 weeks students engagement time
2. Period of Time slot – Between 4th and 5th semester (12 weeks) i.e. commencement of internships will be immediately following the 4th semester exams.
3. Industry area – Engineering Programme Allied industries of large, medium or small-scale, Organization/Govt./ Semi Govt Sectors.

Role(S) of Department

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It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
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Suggestive Format

Following format may be used for training report. Actual format may differ slightly depending upon the nature of Industry/ Organization.

• Title Page
• Certificate
• Abstract
• Acknowledgement
• Content Page

Chapter 1
Organization structure of Industry and general layout.

Chapter 2
Introduction to Industry / Organization (history, type of products and services, turn over and number of employees etc.)

Chapter 3
Types of Major Equipments/raw materials/ instruments/machines/ hardware/software used in industry with their specifications, approximate cost, specific use and routine maintenance done

Chapter 4
Processes/ Manufacturing Manufacturing techniques and methodologies and material handling procedures

Chapter 5
Testing of Hardware/Software/ Raw materials/ Major material handling product (lifts, cranes, slings, pulleys, jacks, conveyor belts etc.) and material handling procedures.

Chapter 6
Safety procedures followed and safety gears used by industry.

Chapter 7
Particulars of Practical Experiences in Industry/Organization if any in Production/Assembly/Testing/Maintenance

Chapter 8
Detailed report of the tasks undertaken (during the training).

Chapter 9
Special/challenging experiences encountered during training if any (may include students liking & disliking of workplaces).

Chapter 10
Conclusion

Chapter 11

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for seminar and project initiation course is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Identify topics of seminar presenting to the large gathering at the institute/conference.
2. Collect relevant and updated research-based data and information to prepare a paper of seminar presentation.
3. Apply presentation skills.
4. Create conducive environment for learning and discussion through seminar presentation.
5. Identify a problem statement and establish the action plan for the successful completion of the project.

General Guidelines

Seminar and Project Initiation

• The seminar must be related to emerging trends in engineering / technology programme or may be inter/ multi-disciplinary, base industry expected outcomes of the programme.
• The individual students have different aptitudes and strengths. Therefore, SEMINAR should match the strengths of students. For purpose, students shall be asked to select the TITLE (Theme)of SEMINAR they would like to prepare and present.
• Seminar titles are to be finalized in consultation with the faculty mentor.
• Seminar must involve logic development of applications of various technologies/ processes applicable in industry.
• Seminar must be assigned to the single student. However, support of other students may be sorted while presenting the seminar
• Students are required to prepare using relevant software tools, write ups for presentation
• Students shall submit One Hard copy and one Soft copy each of the presentation and may be encouraged to keep a recorded cop presentation made during the seminar.
• Batch of 3-4 students shall be formed for project initiation.
• Projects give a platform for the students to showcase an attitude of inquiry to identify the problem statement related to the progr Students shall Identify the information suggesting the cause of the problem and possible solutions
• Students shall study and assess the feasibility of different solutions and the financial implications.
• Students should collect relevant data from different sources (books/internet/market/suppliers/experts through surveys/interviews
• Students shall prepare required drawings/ designs and detailed plan for the successful execution of the work.
• Students may visit the organisation pertaining to the problem statement as part of initial study.

Seminar Preparation and Presentation :

Once the title/topic of a seminar has been finalized and allotted to the student, the teacher’s role is important as guide, mentor an motivator, to promote learning and sustain the interest of the students.
Following should be kept in mind while preparing and presenting the seminar:

• Seminar Orientation cum -briefing: the seminar topics/themes should be innovative, novel and relevant to the curriculum of t programme, and also aligned to the expectations of industry.
• Seminar Literature survey: Information search and data collection: the information and data should be authentic, realistic and r to the curriculum of the programme.
• Seminar Preparation, and presentation: The seminar shall be present with suitable software tools and supporting handout/note presentation of seminar should not be more than 20 minutes including Q-A session.

The following guidelines may be followed for Project Initiation

• Establishing project scope: Determine the boundaries of the project.
• Defining project objectives: Set clear and measurable objectives that align with the project’s purpose.
• Stakeholder identification and analysis: Perform an exercise in identifying all stakeholders involved in the project and analyzi needs and expectations.
• Team Formation: Carefully build a team with the necessary skills and expertise to execute the project successfully.
• Documentation. Create a project planner showcasing the action plan, define the project’s scope, outline the project definition, a design of the project. The document has to be made available to all stakeholders

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for entrepreneurship development and startups is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Identify one’s entrepreneurial traits.
2. Use information collected from stakeholder for establishing/setting up/founding starts up
3. Use support systems available for Starts up
4. Prepare project plans to manage the enterprise effectively

Unit I

Introduction to Entrepreneurship Development 1.1 Entrepreneurship as a career – charms, advantages, disadvantages , scope- local and global 1.2 Traits of successful entrepreneur: consistency, creativity, initiative, independent decision making, assertiveness, persuasion, persistence, information seeking, handling business communication, commitment to work contract, calculated risk taking, learning from failure 1.3 Types of enterprises and their features : manufacturing, service and trading

Suggested Learning Pedagogie
Presentations Lecture Using Chalk-Board

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Unit III

Support System for Startup 3.1 Categorization of MSME, ancillary industries 3.2 Support systems- government agencies: MCED, NI- MSME, PMEGP,DI, KVIC 3.3 Support agencies for entrepreneurship guidance, training, registration, technical consultation, technology transfer and quality control, marketing and finance. 3.4 Breakeven point, return on investment (ROI) and return on sales (ROS).

Suggested Learning Pedagogie
Presentations Lecture Using Chalk-Board

Unit IV

Managing Enterprise 4.1 Techno commercial Feasibility study, feasibility report preparation and evaluation criteria 4.2 Ownership, Capital, Budgeting, Matching entrepreneur with the project 4.3 Unique Selling Proposition [U.S.P.]: Identification, developing a marketing plan. 4.4 Preparing strategies of handling business: policy making, negotiation and bargaining techniques 4.5 Risk Management: Planning for calculated risk taking, initiation with low cost projects, integrated futuristic planning, definition of startup cycle, ecosystem , angel investors, venture capitalist 4.6 Incubation centers and accelerators : Role and procedure

Suggested Learning Pedagogie
Presentations Lecture Using Chalk-Board

List of Experiments:

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Self Learning

Micro Project

• Prepare a ‘ Women entrepreneurship business plan ‘ Choose relevant government scheme for the product/service
• Prepare a ‘Pitch- desk’ for your start up
• Prepare a business plan for a. Market research b. Advertisement agency c. Placement Agency d. Repair and Maintenance agency e. Tour and Travel agency
• Prepare a ‘Social entrepreneurship business plan, plan for CSR funding.
• Prepare a business plan for identified projects by using entrepreneurial eco system for the same (Schemes, incentives, incubators etc.)

Laboratory Equipment

1. Computers with internet and printer facility All

Learning Materials

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Websites

1. http://www.mced.nic.in/allproduct.aspx MCED Product and Plan Details
2. http://niesbud.nic.in/Publication.html The National Institute for Entrepreneurship and Small Business Development Publications
3. http://niesbud.nic.in/docs/1standardized.pdf Courses : The National Institute for Entrepreneurship and Small Business Development
4. https://www.nabard.org/content1.aspx?id=23andcatid=23andmid= 530 Government Schemes
5. https://www.nabard.org/Tenders.aspx?cid=501andid=24 NABARD – Information Centre
6. http://www.startupindia.gov.in/pdffile.php?title=Startup%20I ndia%20Action%20Planandtype=Actionandq=Action%20Plan.pdfandc ontent_type=Actionandsubmenupoint=action Start Up India
7. http://www.ediindia.org/institute.html About – Entrepreneurship Development Institute of India (EDII)
8. http://www.nstedb.com/training/training.htm NSTEDB – Training

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for ai & ml algorithm is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Implement relevant search algorithms as applicable to Artificial Intelligence.
2. Apply method for knowledge representation to make informed decisions for various applications.
3. Analyze different forms of data with respect to different phases of Machine Learning.
4. Create data model for Machine Learning Algorithms.
5. Classify the data by performing different Regression Techniques.

Unit I

Basics of AI and Problem Solving Techniques 1.1 Basic Definition and Terminology: Foundation and Evaluation of AI, Scope of AI, Components of AI, Types of AI, Application of AI 1.2 Intelligent Agent in AI: Types of AI agent, Concept of Rationality, Nature of environment, Structure of agents, Turing Test in AI 1.3 Search Algorithms in Artificial Intelligence: Properties of Search Algorithms, Types of Search Algorithms 1.4 Heuristic Search Techniques: Generate-and-Test; Hill Climbing. Properties of A* algorithm, Depth-First Search, Best-First Search, Greedy Best-First, Problem Reduction 1.5 Beyond Classical Search: Local search algorithms and optimization problem, Local search in continuous spaces, Searching with nondeterministic action and partial observation, Online search agent and unknown environments

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Flipped Classroom

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Unit III

Introduction to ML 3.1 History and Evaluation of ML, AI vs ML 3.2 Machine Learning Life Cycle: Gathering data, Data Preparation, Data Wrangling, Data Analysis, Train Model, Test Model, Deployment 3.3 Different forms of Data: Data Mining, Data Analytics, Statistics Data, Statistics vs. Data Mining, Data Analytics vs Data Science 3.4 Dataset for ML: Training Dataset, Testing Datasets, Training vs Testing 3.5 Data Cleaning: Missing Data, Outliers

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit IV

Types of Learning 4.1 Types of Learning: Supervised, Unsupervised, SemiSupervised Learning 4.2 Supervised Learning: Learning a Class from Examples, Introduction of different types of Supervised Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Tree, K – Nearest Neighbors, Random Forest 4.3 Unsupervised Learning: Introduction of different types of Unsupervised Learning Algorithm: K-means clustering, KNN (k-Nearest Neighbors), Hierarchical Clustering, Neural Networks 4.4 Model evaluation: Introduction of Cross-validation, benefits of cross-validation, Positive and Negative class cross-validation

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

List of Experiments:

1. * Install given Python IDE software and Python “scikit learn” for ML 2
2. Write program to Implement Breadth First Search Algorithm (Uninformed) in Python 2
3. *Write program to implement Depth First Search Algorithm (Uninformed) in Python 2
4. Write program to implement Greedy Best-First (Informed Type) Search Algorithm in python 4
5. * Write program to implement A* search (Informed Type) Algorithm in Python 2
6. * Write program to implement Bayes’ Theorem 4
7. Analyze the given Case study: How Turing test is performed between Responder and an Interrogator? 2
8. * Explore different dataset finders e.g. Google Dataset Search, Kaggle 2
9. * Write program in python to split any dataset into train and tests sets 4
10. Analyze E-mail spam and non-spam filtering using Machine Learning through case study 4
11. *Create and display a Decision Tree on given dataset 2
12. Write program to implement K-means Algorithm 2
13. * Write program to calculate cross validation score for any Dataset like IRIS 2
14. *Write program to implement Simple Linear Regression using Python 2
15. Write program to implement Multiple Linear Regression using Python 2
16. *Write program to create confusion matrix to calculate different measures to quantify the quality of the model 2

Self Learning

Micro Project

• Develop a micro project for Movie Recommendation System: Use a dataset like the MovieLens dataset, preprocess the data (split into training and test sets),train a collaborative filtering model and generate and evaluate recommendations for users.
• Develop a micro project for Simple Chatbot: define a set of intents and responses and train a dataset to classify user inputs.
• Develop a micro project for Spam Email Classifier in which collect a dataset of labelled emails (spam or not spam), pre-process the text data (remove stop words, tokenize, etc.)
• Case study on Natural Language Generation (NLG) for E-commerce Product Descriptions

Other

• Complete the course Artificial Intelligence and Machine Learning on Infosys Springboard.
• Develop a code for given problem suggested by teacher.

Assignment

• Can Artificial Intelligence replace human Intelligence? Justify it
• Describe role of artificial intelligence in banking.
• Compare OpenAI and ChatGPT.
• Identify & List out the equipment / machine available in your Institute where AI technology is used. Describe the role of AI in that equipment.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Materials

1. Stuart Russell and Peter Norvig, Editors Artificial Intelligence A modern Approach Third edition Pearson Education, Inc ISBN-13: 978-0-13604259-4 ISBN-10: 0-13-604259-7
2. Dr. Jeeva Jose Introduction to Machine Learning with Python Khanna Book Publishing Co.(P) Ltd. ISBN 9789389139068 ISBN 9789389139068
3. Dipanjan Sarkar Raghav Bali Tushar Sharma Practical Machine Learning with Python A ProblemSolver’s Guide to Building Real-World Intelligent Systems Apress publication ISBN-13 (pbk): 978-14842-3206-4 ISBN-13 (electronic): 978-1-4842-3207-1
4. Andreas C. Müller & Sarah Guido Introduction to Machine Learning with Python O’Reilly Media, Inc ISBN 9352134575 ISBN 9789352134571
5. Manaramjan Pradhan, U Dinesh Kumar Machine Learning using Python Wiley India ISBN 978-81-265-7990-7 ISBN 9 788126 579907

Learning Websites

1. https://www.python.org/downloads/ Python IDE download
2. https://www.pdfdrive.com/machine-learning-step-by-step-guide -to-implement-machine-learning-algorithms-with-python-d15832 4853.html AI and ML E-Books
3. https://www.geeksforgeeks.org/how-to-install-python-pycharm-on-windows Guidelines for Installation of python
4. https://stackabuse.com/courses/graphs-in-python-theory-and-i mplementation/lessons/a-star-search-algorithm A* algorithm
5. https://www.javatpoint.com/turing-test-in-ai Turing test
6. https://www.v7labs.com/blog/best-free-datasets-for-machine-l earning Datasets
7. https://www.geeksforgeeks.org/how-to-split-a-dataset-into-tr ain-and-test-sets-using-python Training and Testing Dataset
8. https://towardsdatascience.com/email-spam-detection-1-2-b0e0 6a5c0472 Filtering Dataset

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

For all other MSBTE AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for operating system is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Explain the services and components of an Operating System.
2. Describe the different aspects of Process Management in an Operating System.
3. Implement various CPU Scheduling algorithms and evaluate their effectiveness.
4. Analyze the Memory Management techniques used by an Operating System.
5. Apply techniques for effective File Management in an Operating System.

Unit I

Operating System services and components 1.1 Operating System: concept, functions 1.2 Different types of Operating System: Batch Operating System, Multi-programmed, Time Shared Operating System, Multiprocessor System, Distributed System, Real Time System, Mobile OS (Android OS) 1.3 Command line based Operating System: DOS, UNIX GUI based Operating System: WINDOWS, LINUX, MaC OS 1.4 Different Services of Operating System, System Calls: Concept, types of system calls 1.5 Operating System Components: Process Management, Main Memory Management, File Management, IO Management, Secondary Storage Management

Suggested Learning Pedagogie
Presentations Lecture Using Chalk-Board

Unit II

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Unit III

CPU Scheduling 3.1 Scheduling: Basic concept, CPU and I/O burst cycle 3.2 Preemptive and Non-preemptive scheduling, scheduling criteria 3.3 Types of Scheduling algorithms: First Come First Serve(FCFS), Shortest Job First (SJF), Shortest Remaining Time Next (SRTN), Round Robin (RR), Priority Scheduling, Multilevel Queue Scheduling 3.4 Deadlock: System Models, Necessary conditions Leading to Deadlock, Deadlock Handling: Deadlock prevention, Deadlock avoidance- Banker’s Algorithm

Suggested Learning Pedagogie
Presentations Lecture Using Chalk-Board

Unit IV

Memory Management 4.1 Basic Memory Management: Partitioning – Fixed and Variable, Free Space Management Techniques: Bit map, Linked List 4.2 Swapping, Compaction, Fragmentation, Partitioning Algorithms: First fit, Best fit, Worst fit 4.3 Non-contiguous Memory Management Techniques: Paging, Segmentation 4.4 Virtual Memory: Basics, Demand paging, Page Fault 4.5 Page Replacement Algorithm: First In First Out (FIFO), Least Recently Used (LRU), Optimal

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Video Demonstrations

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

List of Experiments:

1. * System call commands in Linux such as fork(), exec(), getpid, pipe, exit, open, close, stat, uname.
2. * Process related commands in Linux – top, ps, kill, wait, sleep, nice, renice,bg,fg.
3. * a. Commands for Sending Messages to Logged-in Users -who, cat, wall, write, mesg. * b. List Processes Attached to a Shared Memory Segment: ipcs.
4. * Write a C/Python program to calculate average waiting time and Turnaround Time of n processes with First Come First Serve (FCFS) CPU scheduling algorithm.
5. Write a C/Python program to calculate average waiting time and Turnaround Time of n processes with Shortest Job First (SJF) CPU scheduling algorit hm.
6. Write a C/Python program to calculate average waiting time and Turnaround Time of n processes with Priority CPU scheduling algorithm.
7. Write a C/Python program to calculate average waiting time and Turnaround Time of n processes with Round Robin (RR) CPU scheduling algorithm.
8. Write a C/Python program to implement Banker’s Algorithm.
9. Basic memory management commands – df, free, vmstat, /proc/meminfo, htop.
10. * Write a C/Python program on First In First Out (FIFO) Page Replacement algorithm.
11. Write a C/Python program on Least Recently Used (LRU) Page Replacement algorithm.
12. * Write a C/Python program on sequential file allocation method.

Self Learning

Assignment

• Find out the total number of page faults using – i) First In First Out ii) Least recently used page replacement ii) Optimal page replacement Page replacement algorithms of memory management, if the page are coming in the order 7

0 1 2 0 3 0 4 2 3 0 3 2 1 2 0 1 7 0 1

• Compare between CLI based Operating System and GUI based Operating System.
• Differentiate between process and thread (any two points). Also discuss the benefits of multithreaded programming.
• Enlist different file allocation methods? Explain contiguous and indexed allocation method in detail.

Micro Project

• Create a report depicting features of different types of operating systems- Batch operating system, Multi programmed, Time shared, Multiprocessor systems, Real time systems, Mobile OS with examples.
• Implement and Compare Memory Allocation Strategies – First Fit, Best Fit, Worst Fit
• Create a report on different operating system tools used to perform various functions.

Self Learning

• Complete any one course related to the operating system on MOOCS such as NPTEL, Coursera, Infosys Springboard etc.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Materials

1. Dhananjay M. Dhamdhere Operating System: A ConceptBased Approach McGraw Hill Education 3rd edition, ISBN: 978-1259005589
2. William Stallings Operating Systems : Internals and Design Principles Pearson Education 9th Edition, ISBN: 978-9352866717
3. Richard Petersen Linux The Complete Reference McGraw Hill, 6th edition, ISBN: 9780071492478
4. Richard Blum Linux command line and shell scripting Wiley India, ISBN: 978-1118983843
5. Abraham Silberschatz and James Peterson Operating System Concepts Wiley India, ISBN: 9781119454083

Learning Websites

1. https://archive.nptel.ac.in/courses/106/105/106105214/ Introduction to Operating System
2. https://www.geeksforgeeks.org/processes-in-linuxunix/ Process Related commands
3. https://ubuntu.com/download/desktop Installation of Ubuntu
4. https://developers.redhat.com/products/rhel/download RedHat Linux download
5. https://www.digitalocean.com/community/tutorials/linux-comma nds Basic Linux commands
6. https://www.geeksforgeeks.org/what-is-an-operating-system/ Operating System

For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 5th Sem Syllabus for K scheme.

For all AI&ML results, visit MSBTE AI&ML all semester results direct links.

Download the iStudy App for all syllabus and other updates.

For detailed Syllabus of all other subjects of AI&ML AN 4th Sem, K scheme do visit Ai&Ml 4th Sem Syllabus for K scheme.

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For the academic calendar of Diploma 4th Sem, kindly visit MSBTE Diploma 4th Sem academic calendar.

Wishing you great luck ahead.