.png&w=1920&q=75)
BCA in Artificial Intelligence Machine Learning at ISBC College of Arts, Science and Commerce
Bengaluru, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Artificial Intelligence & Machine Learning at ISBC College of Arts, Science and Commerce Bengaluru?
This Artificial Intelligence & Machine Learning program at ISBC College of Arts, Science and Commerce focuses on equipping students with theoretical knowledge and practical skills in AI and ML. It is designed to meet the growing demand for skilled professionals in India''''s rapidly expanding technology sector, emphasizing real-world applications and innovation. The program is tailored to bridge the gap between academic learning and industry requirements, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into the dynamic field of artificial intelligence. It also benefits early-career IT professionals looking to specialize in emerging technologies or career changers aiming to transition into data science, machine learning, and AI roles within the Indian industry. Specific prerequisites include a pass in 10+2 or equivalent.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as AI Engineers, Machine Learning Specialists, Data Scientists, and Business Intelligence Analysts. Entry-level salaries typically range from INR 3-6 lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry certifications like TensorFlow Developer and AWS Certified Machine Learning Specialist, fostering robust growth trajectories in various Indian tech startups and established MNCs.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate consistent time to practice C/C++ and Python programming concepts. Focus on logic building, data structures, and object-oriented principles. Regularly solve coding challenges to solidify understanding and develop problem-solving acuity.
Tools & Resources
HackerRank, LeetCode (for beginners), GeeksforGeeks, CodeChef, W3Schools Python Tutorial
Career Connection
A strong programming foundation is essential for any AI/ML role, enabling efficient algorithm implementation and demonstrating core problem-solving abilities critical in technical interviews and development tasks.
Build Strong Mathematical & Statistical Base- (Semester 1-2)
Pay extra attention to linear algebra, calculus, and probability/statistics coursework. These are the underlying pillars of AI/ML algorithms. Form study groups to tackle complex problems and deepen theoretical comprehension.
Tools & Resources
Khan Academy, NPTEL courses on Mathematics for Machine Learning, MIT OpenCourseware, 3Blue1Brown YouTube Channel
Career Connection
A robust mathematical understanding is crucial for comprehending, debugging, and optimizing complex AI/ML models, a key skill for advanced research and development roles within the AI industry.
Engage in Peer Learning & Tech Clubs- (Semester 1-2)
Actively participate in college tech clubs and peer study groups. Discuss concepts, work on mini-projects together, and share resources. This fosters collaborative learning, enhances problem-solving, and builds a supportive academic network.
Tools & Resources
College Tech Clubs, WhatsApp/Telegram study groups, GitHub for collaborative coding, Discord servers for tech communities
Career Connection
Develops teamwork, communication, and networking skills, which are vital for industry projects, collaborative development, and professional growth in a team-oriented tech environment.
Intermediate Stage
Hands-on AI/ML Projects & Kaggle Competitions- (Semester 3-5)
Start working on small AI/ML projects using Python libraries (Scikit-learn, Pandas, NumPy, TensorFlow/Keras). Participate in Kaggle''''s beginner-friendly competitions to apply theoretical knowledge to real-world datasets and hone practical skills.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebooks, GitHub, Udemy/Coursera courses on practical ML
Career Connection
Practical project experience is highly valued by recruiters; Kaggle participation demonstrates problem-solving, competitive coding, and ability to work with real-world data, boosting employability.
Seek Industry Internships & Workshops- (Semester 4-5)
Actively search for internships during semester breaks, focusing on data analysis, AI, or ML roles in Indian startups or tech companies. Attend workshops and seminars to understand industry trends, acquire new tools, and network with professionals.
Tools & Resources
LinkedIn, Internshala, Indeed, College placement cell, Local tech meetups and conferences
Career Connection
Internships provide invaluable industry exposure, build professional networks, and often lead to pre-placement offers, significantly accelerating career entry and professional development.
Specialize in a Niche (NLP/CV/DL)- (Semester 4-5)
As you progress, identify an area within AI/ML (e.g., Natural Language Processing, Computer Vision, Deep Learning) that interests you most. Dedicate extra effort to advanced concepts and build a portfolio around this specialization.
Tools & Resources
TensorFlow, Keras, PyTorch, OpenCV, NLTK, SpaCy
Career Connection
Deep specialization makes you a more attractive candidate for specific, high-demand roles, allowing for focused career growth and expertise in cutting-edge AI sub-fields within the Indian tech landscape.
Advanced Stage
Develop a Capstone AI/ML Project- (Semester 6)
In your final semester, undertake a significant AI/ML project that showcases your cumulative learning. Aim for a novel solution to a real-world problem, incorporating advanced algorithms and tools, from conception to deployment.
Tools & Resources
Cloud platforms (AWS, Azure, GCP), Advanced ML frameworks, Version control (Git), Project management tools (Trello, Jira), Research papers and arXiv
Career Connection
A strong capstone project is a powerful resume builder, demonstrating your ability to conceive, develop, and deploy a complete AI/ML solution to potential employers and serving as a key discussion point in interviews.
Master Interview & Aptitude Skills- (Semester 5-6)
Start rigorous preparation for technical interviews, focusing on data structures, algorithms, system design, and AI/ML concepts specific to your specialization. Practice quantitative aptitude, logical reasoning, and verbal ability for placement tests and company assessments.
Tools & Resources
InterviewBit, LeetCode, Company-specific interview prep guides, General aptitude books/websites (e.g., Indiabix), Mock interviews
Career Connection
Excellent interview and aptitude skills are paramount for securing placements in top tech companies and startups across India, ensuring you can articulate your technical knowledge and problem-solving approach effectively.
Build a Professional Online Presence- (Semester 6 and ongoing)
Create a polished LinkedIn profile, showcasing your projects, skills, certifications, and academic achievements. Maintain a well-organized GitHub repository of your code and project work. Actively network with industry professionals and thought leaders.
Tools & Resources
LinkedIn, GitHub, Personal website/blog (optional), Online portfolio platforms
Career Connection
A strong online presence increases your visibility to recruiters, helps in networking, and acts as a dynamic digital portfolio demonstrating your capabilities and passion for AI/ML to potential employers.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2/PUC or equivalent examination from a recognized board.
Duration: 6 semesters / 3 years
Credits: 160 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UGCBCAT101 | Fundamentals of Computers | Core | 4 | Computer Organization, Input/Output Devices, Memory Hierarchy, Software Concepts, Operating Systems Basics, Data Representation |
| UGCBCAT102 | Programming in C | Core | 4 | C Language Fundamentals, Control Statements, Functions, Arrays, Pointers, Structures and Unions |
| UGCBCAL103 | C Programming Lab | Lab | 2 | Practical C programming exercises, Debugging techniques, Basic algorithm implementation, File handling in C, Pointer applications |
| UGCBCAL104 | Computer Fundamentals and MS Office Lab | Lab | 2 | Windows OS usage, MS Word document creation, MS Excel spreadsheet operations, MS PowerPoint presentations, Internet browsing and email |
| AECC-ENG1 | English-I | Ability Enhancement Compulsory Course | 3 | Basic English Grammar, Reading Comprehension, Paragraph Writing, Basic Communication Skills, Vocabulary Building |
| AECC-IND1 | Kannada/Other Indian Language-I | Ability Enhancement Compulsory Course | 3 | Basic grammar of chosen language, Reading simple texts, Conversational skills, Introduction to literature, Cultural aspects |
| UGCBCASEC1.1 | Web Designing Fundamentals | Skill Enhancement Course | 3 | HTML structure and elements, CSS for styling, Basic JavaScript, Responsive Design principles, Web page layout |
| OE1 | Open Elective - 1 | Open Elective | 3 | General Elective Topics (e.g., Entrepreneurship Development, Financial Accounting), Interdisciplinary concepts, Application-oriented learning, Problem-solving approaches |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UGCBCAT201 | Data Structures | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees and Binary Trees, Graphs, Searching and Sorting Algorithms |
| UGCBCAT202 | Object Oriented Programming using C++ | Core | 4 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Virtual Functions, Exception Handling |
| UGCBCAL203 | Data Structures Lab | Lab | 2 | Implementation of data structures, Algorithm analysis, Stack and Queue applications, Linked list operations, Tree traversal algorithms |
| UGCBCAL204 | OOP with C++ Lab | Lab | 2 | Practical OOP concepts, Class design and implementation, Inheritance and Polymorphism examples, File I/O in C++, Template programming |
| AECC-ENG2 | English-II | Ability Enhancement Compulsory Course | 3 | Advanced English Grammar, Report Writing, Public Speaking Skills, Letter Writing, Literary Analysis |
| AECC-IND2 | Kannada/Other Indian Language-II | Ability Enhancement Compulsory Course | 3 | Intermediate grammar, Short stories and poetry, Translation exercises, Formal communication, Cultural understanding |
| UGCBCASEC2.1 | Python Programming Fundamentals | Skill Enhancement Course | 3 | Python Syntax and Data Types, Control Flow, Functions and Modules, File I/O, Object-Oriented Python |
| OE2 | Open Elective - 2 | Open Elective | 3 | General Elective Topics, Cross-disciplinary studies, Skill development, Current affairs applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UGCBCAT301 | Database Management Systems | Core | 4 | DBMS Architecture, ER Model, Relational Model, SQL Queries, Normalization, Transactions and Concurrency Control |
| UGCBCAT302 | Operating Systems | Core | 4 | OS Functions and Types, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| UGCBCAT303 | Computer Networks | Core | 4 | Network Topologies, OSI Model, TCP/IP Model, Networking Devices (Hub, Switch, Router), IP Addressing and Subnetting, Routing Protocols |
| UGCBCAL304 | DBMS Lab | Lab | 2 | SQL query writing, Database design and implementation, ER diagrams to relational schema, Trigger and stored procedure creation, Report generation |
| UGCBCAL305 | OS & Networks Lab | Lab | 2 | Shell scripting, Process management commands, Network configuration tools, Socket programming basics, Network utility commands |
| UGCBCASEC3.1 | Mobile Application Development | Skill Enhancement Course | 3 | Android Studio basics, UI design principles, Activity Lifecycle, Data Storage options, Basic app development with Java/Kotlin |
| OE3 | Open Elective - 3 | Open Elective | 3 | General Elective Topics, Sector-specific knowledge, Analytical thinking, Problem-solving methodologies |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UGCBCAT401 | Java Programming | Core | 4 | Java Fundamentals, Classes and Objects, Inheritance and Interfaces, Packages and Exception Handling, Multithreading, GUI Programming (AWT/Swing/JavaFX) |
| UGCBCAT402 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis and Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms (BFS, DFS, Dijkstra), NP-Completeness |
| UGCBCAL403 | Java Programming Lab | Lab | 2 | Practical Java applications, GUI development, Database connectivity (JDBC), Thread synchronization, Applet/Servlet basics |
| UGCBCAL404 | Algorithms Lab | Lab | 2 | Implementation of sorting algorithms, Graph traversal algorithms, Dynamic programming solutions, Greedy algorithm implementations, Divide and conquer techniques |
| UGCBCADEC4.1 | Introduction to Artificial Intelligence | Specialization Elective | 4 | AI History and Foundations, Intelligent Agents, Problem Solving through Search (DFS, BFS, A*), Knowledge Representation, Expert Systems, Game Playing (Minimax) |
| UGCBCADEC4.2 | Machine Learning Fundamentals | Specialization Elective | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms (Decision Trees, SVM), Clustering Techniques (K-Means), Model Evaluation Metrics |
| UGCBCASEC4.1 | Cloud Computing Concepts | Skill Enhancement Course | 3 | Cloud Computing Models (IaaS, PaaS, SaaS), Deployment Models (Public, Private, Hybrid), Virtualization Technologies, Cloud Security, Introduction to AWS/Azure/GCP |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UGCBCAT501 | Data Science with Python | Core | 4 | Introduction to Data Science, NumPy for numerical operations, Pandas for data manipulation, Matplotlib and Seaborn for data visualization, Data Cleaning and Preprocessing, Statistical Analysis with Python |
| UGCBCAT502 | Advanced Database Management Systems | Core | 4 | NoSQL Databases (MongoDB, Cassandra), Distributed Databases, Data Warehousing Concepts, Data Mining Fundamentals, Big Data Introduction, Database Security |
| UGCBCAL503 | Data Science Lab | Lab | 2 | Practical data manipulation with Pandas, Data visualization projects, Implementing basic statistical models, Data preprocessing tasks, Exploratory Data Analysis |
| UGCBCADEC5.1 | Deep Learning | Specialization Elective | 4 | Neural Networks and Perceptrons, Backpropagation Algorithm, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transfer Learning, TensorFlow/Keras Frameworks |
| UGCBCADEC5.2 | Natural Language Processing | Specialization Elective | 4 | Text Preprocessing, Tokenization and Stemming, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Introduction to NLTK/SpaCy |
| UGCBCAP5.1 | Project Work Phase I / Internship | Project/Internship | 4 | Problem identification and definition, Literature survey, System design, Methodology and tools selection, Preliminary report writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UGCBCAT601 | Web Technologies with Frameworks | Core | 4 | Frontend Frameworks (React/Angular/Vue), Backend Frameworks (Node.js/Django/Spring Boot), REST APIs, MVC Architecture, Database Integration, Authentication and Authorization |
| UGCBCADEC6.1 | Computer Vision | Specialization Elective | 4 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition, OpenCV Library |
| UGCBCADEC6.2 | AI Ethics and Governance | Specialization Elective | 4 | Ethical AI principles, Bias and Fairness in AI, Data Privacy and Security in AI, AI Regulations and Policies (GDPR, India''''s DPDP Bill), Accountability and Transparency in AI, Societal impact of AI |
| UGCBCAP6.1 | Project Work Phase II | Project | 6 | Implementation of proposed system, Testing and debugging, Performance evaluation, Documentation and report finalization, Project presentation and demonstration |
| OE4 | Open Elective - 4 | Open Elective | 3 | General Elective Topics, Advanced interdisciplinary studies, Emerging technology trends, Global market insights |
