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MCA in Artificial Intelligence Machine Learning at Akash Global College of Management and Science
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Akash Global College of Management and Science Bengaluru?
This MCA program at Akash Global College, through its carefully chosen elective pathways and core subjects, allows students to cultivate a strong focus on Artificial Intelligence & Machine Learning. The curriculum is designed to equip students with foundational and advanced concepts crucial for the evolving Indian tech landscape, emphasizing both theoretical understanding and practical application in AI and ML domains.
Who Should Apply?
This program is ideal for aspiring software developers, data analysts, and computer science graduates seeking entry into the high-demand fields of AI and ML in India. It also suits working professionals aiming to upskill and transition into roles requiring expertise in intelligent systems, predictive modeling, and data-driven decision-making.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths such as AI Engineer, Machine Learning Developer, Data Scientist, or Business Intelligence Analyst. Entry-level salaries typically range from INR 4-8 LPA, with significant growth trajectories for experienced professionals in Indian IT companies, startups, and research organizations.
Student Success Practices
Foundation Stage
Strengthen Core Programming Skills with Python- (Semester 1-2)
Dedicate time in the initial semesters to master Python programming, focusing on data structures, algorithms, and object-oriented principles. Python is the backbone of most AI/ML development, and a strong command will be invaluable.
Tools & Resources
HackerRank, LeetCode, DataCamp, Codecademy (for Python)
Career Connection
Proficiency in Python directly enhances employability for AI/ML roles requiring coding and scripting skills, often a primary filtering criterion in Indian tech companies.
Build a Robust Mathematical and Statistical Foundation- (Semester 1-2)
Focus intently on Discrete Mathematics, Statistics, and Data Structures courses. AI and ML are deeply rooted in these concepts, and a solid understanding will make learning complex algorithms much easier.
Tools & Resources
Khan Academy, NPTEL courses on Mathematics for ML, MIT OpenCourseware
Career Connection
A strong mathematical base is essential for understanding algorithm mechanics, debugging models, and pursuing advanced research or specialized ML roles in India.
Engage in Early Data Analysis Mini-Projects- (Semester 2-3)
Apply newly acquired programming and statistical knowledge to small data analysis projects. Start with public datasets (e.g., from Kaggle) to practice data cleaning, exploration, and basic visualization.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, NumPy, Pandas
Career Connection
Early practical experience helps build a portfolio, demonstrates initiative, and prepares students for the data-centric nature of AI/ML roles prevalent in the Indian job market.
Intermediate Stage
Deep Dive into Core AI/ML Electives- (Semester 3-4)
Consciously choose and thoroughly engage with AI/ML-focused electives such as Artificial Intelligence, Soft Computing, Data Analytics, and Data Warehousing & Mining. Aim to understand the underlying principles and practical applications.
Tools & Resources
Online courses (Coursera, edX) complementing syllabus, Relevant research papers and industry blogs
Career Connection
Mastering these specialized subjects is crucial for building a strong technical profile for AI/ML positions and showcasing expertise to potential employers in India.
Participate in AI/ML Hackathons and Competitions- (Semester 3-4)
Actively participate in university-level or national hackathons and coding competitions focused on AI and Machine Learning. This provides hands-on experience, problem-solving skills, and networking opportunities.
Tools & Resources
Devpost, Kaggle Competitions, GitHub
Career Connection
Success in competitions and collaborative project work significantly enhances a resume, demonstrating practical application skills and teamwork, highly valued by Indian tech recruiters.
Develop a Personal AI/ML Portfolio on GitHub- (Semester 3-4)
Create a public GitHub repository to showcase all AI/ML-related projects, lab assignments, and competition entries. Document code clearly and explain methodologies and results.
Tools & Resources
GitHub, ReadMe files, Google Colab notebooks
Career Connection
A well-maintained GitHub portfolio is a critical asset for job applications in India, allowing recruiters to assess coding ability and project experience directly.
Advanced Stage
Undertake an Industry-Relevant Final Project- (Semester 4)
For the Semester 4 Project Work, choose a topic that applies AI/ML techniques to solve a real-world industry problem. Aim for a solution with tangible outcomes, potentially in collaboration with a local company or startup.
Tools & Resources
Latest research papers, Industry reports, Mentors from academia/industry
Career Connection
A robust, industry-relevant final project is a cornerstone for securing placements, particularly for roles requiring specialized AI/ML problem-solving in the Indian context.
Prepare for Technical Interviews and Aptitude Tests- (Semester 4)
Dedicate significant time to practicing common AI/ML interview questions, data structure and algorithm problems, and general aptitude tests. Focus on explaining concepts clearly and confidently.
Tools & Resources
GeeksforGeeks, InterviewBit, Glassdoor for company-specific interview experiences
Career Connection
Thorough preparation for technical interviews is paramount for placement success in India''''s competitive IT and data science job market.
Network and Seek Mentorship in the AI/ML Community- (Semester 4)
Actively attend industry webinars, conferences, and local meetups related to AI/ML. Connect with professionals, alumni, and faculty to gain insights and mentorship for career guidance.
Tools & Resources
LinkedIn, Meetup groups, Professional conferences (e.g., Data Science Congress)
Career Connection
Networking opens doors to internship opportunities, industry contacts, and provides valuable career advice, aiding in better placement and professional growth in India.
Program Structure and Curriculum
Eligibility:
- Bachelor’s Degree (BCA/B.Sc/B.Com/B.A with Mathematics at 10+2 level or at Graduation level) with at least 50% aggregate marks (45% for reserved categories). Valid score in KEA PGCET / Any other state-level entrance examination.
Duration: 2 years (4 semesters)
Credits: 106 Credits
Assessment: Internal: 40% (for Theory), 50% (for Practicals), External: 60% (for Theory), 50% (for Practicals)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCA101T | Object Oriented Programming with C++ | Core | 4 | C++ Fundamentals, Classes and Objects, Inheritance and Polymorphism, Templates and Exception Handling, File I/O |
| MCA102T | Discrete Mathematics and Statistics | Core | 4 | Set Theory and Logic, Relations and Functions, Graphs and Trees, Probability and Distributions, Correlation and Regression |
| MCA103T | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms, Algorithm Analysis |
| MCA104T | Computer Organization and Architecture | Core | 4 | Digital Logic Circuits, Basic Computer Organization, CPU Design and Pipelining, Memory Hierarchy, I/O Organization |
| MCA105L | Object Oriented Programming with C++ Lab | Lab | 2 | C++ Program Development, Class and Object Implementation, Inheritance and Polymorphism Exercises, Operator Overloading, File Handling |
| MCA106L | Data Structures and Algorithms Lab | Lab | 2 | Array and Linked List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| MCA107S | Soft Skills | Skill Enhancement | 2 | Communication Skills, Presentation Skills, Teamwork and Collaboration, Time Management, Interpersonal Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCA201T | Operating Systems | Core | 4 | OS Introduction, Process Management, Memory Management, File Systems, Deadlocks and Concurrency |
| MCA202T | Database Management Systems | Core | 4 | DBMS Concepts, ER Modeling, Relational Model and Algebra, SQL Queries, Transaction Management |
| MCA203T | Computer Networks | Core | 4 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport and Application Layers |
| MCA204T | Web Technologies | Core | 4 | HTML5 and CSS3, JavaScript Fundamentals, XML and AJAX, Server-side Scripting (PHP/ASP.NET basics), Web Frameworks Introduction |
| MCA205L | Database Management Systems Lab | Lab | 2 | SQL Commands Practice, Database Schema Design, Query Optimization, PL/SQL Programming, Report Generation |
| MCA206L | Web Technologies Lab | Lab | 2 | HTML/CSS Page Design, JavaScript Interactive Elements, Form Validation, Dynamic Web Pages, Basic Server-Side Scripting |
| MCA207R | Research Methodology | Ability Enhancement | 2 | Research Problem Formulation, Research Design, Data Collection Methods, Statistical Analysis for Research, Report Writing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCA301T | Software Engineering | Core | 4 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Testing Strategies, Project Management |
| MCA302T | Java Programming | Core | 4 | Java Fundamentals, Object-Oriented Programming in Java, Exception Handling, Multithreading, GUI Programming (AWT/Swing) |
| MCA303T | Cloud Computing | Core | 4 | Cloud Computing Concepts, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization, Cloud Security |
| MCA304EL1 | Python Programming | Elective (chosen for AI/ML focus) | 4 | Python Basics, Data Structures in Python, Functions and Modules, File I/O and Exception Handling, Introduction to Libraries (NumPy, Pandas) |
| MCA305L | Java Programming Lab | Lab | 2 | Java Program Development, Class and Object Implementations, Exception Handling Practice, Thread Synchronization, GUI Applications |
| MCA306L | Cloud Computing Lab | Lab | 2 | Cloud Service Provisioning, Virtual Machine Deployment, Cloud Storage Services, PaaS Application Deployment, Containerization Basics |
| MCA307M | Mini Project | Project | 2 | Project Planning, Software Development Life Cycle, Requirements Gathering, Design and Implementation, Testing and Documentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCA401T | Cyber Security | Core | 4 | Security Principles, Cryptography, Network Security, Web Security, Cyber Forensics |
| MCA402T | Optimization Techniques | Core | 4 | Linear Programming, Transportation and Assignment Problems, Dynamic Programming, Queuing Theory, Game Theory |
| MCA403T | Data Analytics | Core (AI/ML relevant) | 4 | Data Preprocessing, Exploratory Data Analysis, Statistical Methods for Data Analysis, Regression Analysis, Clustering Techniques |
| MCA404T | Soft Computing | Core (AI/ML relevant) | 4 | Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, Hybrid Systems, Neuro-Fuzzy Systems |
| MCA405EL2 | Artificial Intelligence | Elective (chosen for AI/ML focus) | 4 | AI Fundamentals, Problem Solving Agents, Knowledge Representation, Uncertainty and Probabilistic Reasoning, Machine Learning Basics |
| MCA406EL3 | Data Warehousing and Mining | Elective (chosen for AI/ML focus) | 4 | Data Warehousing Concepts, OLAP Operations, Data Mining Techniques, Association Rule Mining, Classification and Prediction |
| MCA407L | Cyber Security Lab | Lab | 2 | Network Scanning Tools, Cryptography Implementation, Firewall Configuration, Vulnerability Assessment, Incident Response Simulation |
| MCA408L | Data Analytics Lab | Lab (AI/ML relevant) | 2 | Statistical Software (R/Python), Data Visualization, Regression Model Building, Clustering Implementation, Data Cleaning and Transformation |
| MCA409P | Project Work | Project | 12 | System Design, Software Implementation, Testing and Debugging, Project Documentation, Presentation and Viva-voce |
