Course Overview
AI/ML courses provide structured programs teaching the knowledge and skills needed to work with artificial intelligence and machine learning, covering core concepts, mathematics, algorithms, and specialized fields like NLP, computer vision, and reinforcement learning.
What You Will Learn
Foundation Years (Semester I–III)
These semesters build strong fundamentals in computing and programming.
Semester I
- Fundamentals of Information Technology
- Problem Solving using C
- Computational Mathematics
- MS Office Tools, Word, Excel, Access, PowerPoint Labs
- C Programming Lab
Semester II
- Data Structures using C
- Database Management System (SQL & PL/SQL)
- Computer Architecture
- DBMS Lab, Data Structures Lab
Semester III
- Object-Oriented Programming using Java
- Operating Systems
- Computer Networks
- OOP Lab, OS Lab
- Electives: Digital Marketing / Web Content Management / DevOps
AI/ML Core and Tools (Semester IV–VI)
Semester IV
- Artificial Intelligence – I
(Covers introduction to AI concepts and problem-solving) - Python Programming
(Important for ML/AI applications) - Software Engineering
- AI Lab, Python Lab
- Electives: Cloud Computing / OOAD / Digital Image Processing
- Internet Basics (Compulsory)
Semester V
- Machine Learning Techniques
- Web Development
- Data Analytics using R
- Internet of Things (IoT)
- Artificial Intelligence – II
- Natural Language Processing (NLP)
- Labs: ML & IoT Lab, Web Dev Lab, R Lab
- AI Tools & Techniques (Compulsory)
Semester VI
- Artificial Neural Networks
- Deep Learning
- Computer Vision
- Cyber Security
- Project Work (Capstone project to apply ML/AI skills)
Study Options:
Practical Skills Developed
- Programming Languages: C, Java, Python
- ML Tools & Libraries: R, Python (NumPy, Pandas, Scikit-learn, etc.)
- Databases: SQL, PL/SQL, Oracle
- Web Tech: HTML/CSS/JS, Web development tools
- AI/ML Applications: Classification, NLP, ANN, CNN, IoT integration
- Project Development: End-to-end solutions with real-world datasets
Career/Skill Outcomes
By the end of the course, students are expected to:
- Understand and implement AI/ML algorithms
- Use data analytics tools and visualization
- Build ML models using Python/R
- Develop AI-powered applications (e.g., chatbots, prediction systems)
- Be ready for roles like AI Developer, ML Engineer, Data Analyst, Python Developer, RPA Analyst, etc.