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BCA in Artificial Intelligence Machine Learning at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This Artificial Intelligence & Machine Learning program at Koneru Lakshmaiah University focuses on equipping students with cutting-edge skills in intelligent systems development. Given India''''s burgeoning digital economy, there''''s a significant demand for AI/ML experts across sectors like IT, healthcare, and finance. This program distinguishes itself by combining theoretical foundations with extensive hands-on practical experience, fostering innovation.
Who Should Apply?
This program is ideal for 10+2 graduates with a strong aptitude for mathematics and problem-solving, seeking entry into high-growth tech domains. It also suits working professionals aiming to upskill in AI/ML or career changers transitioning into the data science industry. Aspiring researchers and innovators in AI and those with a keen interest in computational logic will find the curriculum stimulating.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as AI Engineers, Machine Learning Scientists, Data Analysts, or NLP Specialists. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more in leading companies. The program prepares students for industry certifications and provides a solid foundation for higher studies or entrepreneurship in the AI sector.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus intensely on C and Java programming, understanding data structures and object-oriented concepts thoroughly. Utilize online coding platforms to practice regularly and solve competitive programming problems to solidify problem-solving logic.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Sololearn
Career Connection
Strong foundational coding skills are essential for all tech roles and form the base for advanced AI/ML algorithm implementation, critical for technical interviews.
Build Strong Mathematical Acumen- (Semester 1-2)
Pay close attention to Mathematical Foundations and Digital Electronics. Reinforce concepts through problem-solving and understanding the underlying logic, which are crucial for comprehending and developing AI algorithms and hardware interactions.
Tools & Resources
Khan Academy, NPTEL courses on Discrete Mathematics, Textbook exercises
Career Connection
A deep understanding of linear algebra, calculus, and discrete mathematics is vital for comprehending, optimizing, and developing complex AI/ML models.
Engage in Peer Learning & Early Projects- (Semester 1-2)
Form study groups to discuss complex topics, share insights, and collaborate on small academic projects. Begin exploring basic Python for data manipulation and scripting even before it''''s formally taught to get a head start in AI/ML.
Tools & Resources
GitHub for code sharing, Collaborative online whiteboards, Kaggle for beginner datasets
Career Connection
Teamwork, effective communication, and early exposure to practical problem-solving are highly valued by employers and help in building an initial project portfolio.
Intermediate Stage
Apply Core Concepts to Real-World Problems- (Semester 3-4)
Actively seek opportunities to implement concepts learned in Data Structures, DBMS, and Operating Systems through mini-projects. For AI/ML electives, work on small datasets from platforms like Kaggle to build initial models and understand practical implications.
Tools & Resources
SQL Fiddle, MongoDB Atlas (free tier), Jupyter Notebook, Scikit-learn
Career Connection
Practical application solidifies theoretical understanding and develops critical problem-solving skills, highly sought after for entry-level engineering and data analyst roles.
Dive Deep into AI/ML Specializations- (Semester 4-5)
Beyond classroom learning, enroll in online courses or participate in hackathons focused on machine learning algorithms, deep learning, and natural language processing. Build a robust portfolio of projects using Python libraries like TensorFlow and PyTorch.
Tools & Resources
Coursera (DeepLearning.AI), fast.ai, TensorFlow, PyTorch, Keras, Hugging Face
Career Connection
Specialized knowledge and a strong project portfolio are critical for securing AI/ML specific internships and jobs in India''''s competitive tech landscape.
Network and Seek Industry Exposure- (Semester 3-5)
Attend webinars, workshops, and industry talks by AI/ML practitioners. Connect with alumni and professionals on LinkedIn. Actively look for summer internships or part-time projects in relevant companies to gain real-world experience and build a professional network.
Tools & Resources
LinkedIn, KLU Alumni Network, Industry conferences (virtual/local)
Career Connection
Networking opens doors to mentorship, internships, and full-time employment opportunities, providing crucial industry insights and accelerating career growth.
Advanced Stage
Intensify Project-Based Learning & Research- (Semester 6)
Devote significant effort to your major project (Project II) by selecting a challenging AI/ML problem. Aim for a deployable solution, and consider publishing research papers in national conferences. Explore advanced topics like Computer Vision and Big Data Analytics through hands-on implementation.
Tools & Resources
Google Colab, AWS/Azure free tiers, Docker, Git, LaTeX for thesis writing
Career Connection
A strong final year project is a powerful resume booster, demonstrating independent research, problem-solving, and implementation skills to potential employers in India.
Prepare Rigorously for Placements & Higher Studies- (Semester 6)
Start comprehensive placement preparation, including aptitude tests, technical interviews covering data structures, algorithms, and advanced AI/ML concepts. Engage in mock interviews. For higher studies, prepare for competitive exams like GRE/GATE and research potential postgraduate programs.
Tools & Resources
InterviewBit, LeetCode premium, Glassdoor, KLU Placement Cell resources
Career Connection
Targeted preparation significantly increases chances of securing desirable job offers from top Indian tech companies or admission to leading postgraduate programs in AI/ML.
Specialize and Certify- (Semester 6)
Deepen expertise in a chosen sub-field like Computer Vision or Big Data Analytics through advanced learning. Pursue relevant professional certifications from industry leaders such as Google, AWS, or NVIDIA to validate your specialized skills and enhance marketability.
Tools & Resources
Google Cloud AI Engineer Certification, AWS Machine Learning Specialty, NVIDIA Deep Learning Institute courses
Career Connection
Specialization and professional certifications make you a highly competitive candidate, differentiating you in the Indian job market for niche and high-paying AI/ML roles.
Program Structure and Curriculum
Eligibility:
- A Pass in 10+2 or equivalent examination with minimum 50% Marks and must have studied Mathematics / Statistics / Computer Science / Information Technology / Informatics Practices as one of the subjects.
Duration: 3 years / 6 semesters
Credits: 140 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BC11C01 | Problem Solving through C | Core | 4 | Introduction to C, Operators and Expressions, Control Statements, Arrays and Strings, Functions and Pointers, Structures and Unions |
| 20BC11C02 | Mathematical Foundations of Computer Science | Core | 4 | Set Theory, Mathematical Logic, Relations and Functions, Graph Theory, Algebraic Structures, Recurrence Relations |
| 20HS11F01 | English I | Foundation | 3 | Vocabulary Building, Grammar Review, Reading Comprehension, Paragraph and Essay Writing, Listening and Speaking Skills |
| 20ES11L01 | Digital Electronics Lab | Lab | 1 | Logic Gates verification, Boolean Algebra implementation, Combinational Circuits design, Sequential Circuits implementation, Counters and Registers, Decoders and Encoders |
| 20BC11L01 | Problem Solving through C Lab | Lab | 1 | Basic C Programs, Conditional and Looping statements, Array and String operations, Function calls and Pointers, Structure and Union manipulation, File Handling exercises |
| 20ES11C01 | Digital Electronics | Core | 4 | Number Systems, Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Registers and Counters, Memory and Programmable Logic |
| 20HS11F02 | Environmental Science | Foundation | 3 | Ecosystems, Biodiversity and its Conservation, Environmental Pollution, Natural Resources, Social Issues and the Environment, Human Population and Environment |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BC12C01 | Data Structures | Core | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs, Sorting and Searching Algorithms |
| 20BC12C02 | Object Oriented Programming through Java | Core | 4 | Java Fundamentals, Classes and Objects, Inheritance and Polymorphism, Packages and Interfaces, Exception Handling, Multithreading and Collections |
| 20BC12C03 | Computer Organization and Architecture | Core | 4 | Basic Computer Organization, Central Processing Unit, Memory System, Input/Output Organization, Pipelining and Parallel Processing, Control Unit Design |
| 20HS12F01 | English II | Foundation | 3 | Advanced Communication Skills, Technical Report Writing, Presentation Skills, Group Discussion Techniques, Interview Skills, Effective Public Speaking |
| 20BC12L01 | Data Structures Lab | Lab | 1 | Array and Linked List implementations, Stack and Queue applications, Binary Search Tree operations, Graph Traversal algorithms, Sorting algorithms implementation, Searching algorithms implementation |
| 20BC12L02 | Object Oriented Programming through Java Lab | Lab | 1 | Java Basics programs, Class and Object creation, Inheritance and Polymorphism examples, Package and Interface usage, Exception Handling scenarios, Multithreading and GUI applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BC21C01 | Operating Systems | Core | 4 | Introduction to Operating Systems, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Protection |
| 20BC21C02 | Database Management Systems | Core | 4 | Introduction to DBMS, Entity-Relationship Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| 20BC21C03 | Data Communication and Computer Networks | Core | 4 | Data Communication Basics, Network Models (OSI, TCP/IP), Physical Layer, Data Link Layer, Network Layer, Transport and Application Layers |
| 20BC21L01 | Operating Systems Lab | Lab | 1 | Linux Commands and Utilities, Shell Scripting, Process Creation and Management, CPU Scheduling algorithms, Memory Allocation strategies, Synchronization problems |
| 20BC21L02 | Database Management Systems Lab | Lab | 1 | SQL DDL Commands, SQL DML Commands, Joins and Subqueries, Views and Sequences, Stored Procedures and Functions, Triggers and Cursors |
| 20BC21S01 | Introduction to Artificial Intelligence and Machine Learning | Specialization Elective | 4 | Introduction to AI, Problem-Solving Methods, Knowledge Representation, Introduction to Machine Learning, Supervised Learning Basics, Unsupervised Learning Basics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BC22C01 | Software Engineering | Core | 4 | Software Process Models, Requirements Engineering, Software Design, Software Testing, Software Project Management, Software Maintenance |
| 20BC22C02 | Web Technologies | Core | 4 | HTML5 and CSS3, JavaScript Fundamentals, XML and AJAX, Web Servers, Server-side Scripting Basics, Web Security Principles |
| 20BC22C03 | Python Programming | Core | 4 | Python Basics, Data Structures in Python, Functions and Modules, Object-Oriented Programming, File Handling, Error Handling and Debugging |
| 20BC22L01 | Software Engineering Lab | Lab | 1 | Requirement Gathering tools, UML Diagramming tools, Software Design patterns, Testing Tools (e.g., Selenium), Version Control systems (Git), Project Management tools |
| 20BC22L02 | Web Technologies Lab | Lab | 1 | HTML/CSS webpage development, JavaScript for interactivity, DOM Manipulation, Form Validation, AJAX requests, Basic Server-side scripting |
| 20BC22L03 | Python Programming Lab | Lab | 1 | Python Basic syntax, List, Tuple, Dictionary operations, Function definition and calls, Class and Object creation, File I/O operations, Exception handling programs |
| 20BC22S01 | Machine Learning Algorithms | Specialization Elective | 4 | Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, K-Means Clustering, Ensemble Methods |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BC31C01 | Principles of Management | Core | 3 | Introduction to Management, Planning and Decision Making, Organizing and Staffing, Directing and Motivation, Controlling, Leadership and Communication |
| 20BC31C02 | Mobile Application Development | Core | 4 | Introduction to Mobile Development, Android Architecture, UI Design with Activities, Data Storage and SQLite, Networking and Web Services, Publishing Applications |
| 20BC31L01 | Mobile Application Development Lab | Lab | 1 | Android Studio setup, Activity Lifecycle management, Layouts and Widgets, Database integration (SQLite), Web API consumption, Notifications and Broadcast Receivers |
| 20BC31S01 | Deep Learning | Specialization Elective | 4 | Neural Networks Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Optimization Algorithms, Deep Learning Frameworks (TensorFlow/PyTorch), Generative Adversarial Networks (GANs) |
| 20BC31S02 | Natural Language Processing | Specialization Elective | 4 | NLP Fundamentals, Text Pre-processing and Tokenization, Language Modeling, Syntactic Analysis, Semantic Analysis, NLP Applications (Sentiment, Translation) |
| 20BC31S03 | Reinforcement Learning | Specialization Elective | 4 | Introduction to Reinforcement Learning, Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference Learning, Deep Reinforcement Learning |
| 20BC31PR1 | Project - I | Project | 2 | Problem Identification, Literature Survey, Requirements Analysis, System Design, Project Proposal, Mid-term Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BC32C01 | Industrial Management and Entrepreneurship | Core | 3 | Principles of Industrial Management, Production and Operations Management, Marketing Management, Financial Management, Entrepreneurship Development, Business Plan Preparation |
| 20BC32PR1 | Project - II | Project | 10 | Project Implementation, Testing and Debugging, System Integration, Documentation and Report Writing, Final Presentation and Viva-voce, Deployment Strategies |
| 20BC32S01 | Computer Vision | Specialization Elective | 4 | Image Formation and Perception, Image Processing Techniques, Feature Detection and Description, Object Recognition and Detection, Image Segmentation, 3D Vision and Motion Analysis |
| 20BC32S02 | Big Data Analytics | Specialization Elective | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, Apache Spark, Data Warehousing Concepts, Data Mining Techniques |
