B Tech In Ai Ml 2026 - Colleges, Fees, Eligibility, Syllabus

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HomeCoursesB.Tech in AI/ML
Bachelor4 Years

Bachelor of Technology (B.Tech) in Artificial Intelligence and Machine Learning

4 Years
₹2.0L/Year
B.Tech in AI/ML in India is a 4-year engineering degree designed for students who want careers in machine learning, data science, and artificial intelligence. Explore eligibility, syllabus, fees, entrance exams, top recruiters, and job roles after B.Tech in Artificial Intelligence and Machine Learning.

About B.Tech in AI/ML

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Content Team

Content Writer | Updated 17 Mar 2026

B.Tech in AI/ML is a 4-year undergraduate engineering program focused on building intelligent systems using data, algorithms, and computing. It blends core computer science and engineering fundamentals with machine learning, deep learning, and data engineering. The course prepares students for roles in AI product development, analytics, and applied research across industries.
B.Tech in Artificial Intelligence and Machine Learning in India is a 4-year engineering degree that trains students to design, build, and deploy AI-driven applications. The curriculum typically starts with engineering mathematics, programming, data structures, digital logic, and computer organization, then progresses to probability, statistics, database systems, operating systems, and computer networks. Dedicated AI/ML coverage includes supervised and unsupervised learning, deep learning, natural language processing, computer vision, reinforcement learning, and model evaluation. Many colleges also include data engineering, cloud fundamentals, MLOps basics, ethics and responsible AI, and domain electives such as healthcare analytics or fintech. Students complete lab work, mini-projects, internships, and a final-year capstone focused on real datasets and end-to-end model deployment. In India, admissions are commonly through JEE-based counselling or state/private university entrance tests, and the program aligns with industry demand in IT services, product companies, startups, manufacturing, and BFSI.
Industry-relevant AI/ML curriculum with labs, mini-projects, and a final-year capstone
Strong foundation in core CS subjects (DSA, OS, DBMS, Networks) along with deep learning, NLP, and computer vision
Career pathways in data science, ML engineering, and AI product development across sectors

Eligibility Criteria

1

Educational Qualification

10+2 (or equivalent) with Physics and Mathematics as compulsory subjects along with one of Chemistry/Computer Science/Electronics/Information Technology/Biology (as per institute norms); minimum aggregate typically 50-60% (45-50% for reserved categories as applicable). Admission generally requires a valid score in JEE Main or relevant state/university entrance exam, followed by counselling/seat allotment. Some private universities may offer direct admission based on merit/interview.

2

Entrance Exam

JEE Main, BITSAT

Admission Process

How to Apply

Admission is typically through entrance exam scores (JEE Main/state tests/university exams) followed by counselling and seat allotment. Candidates submit documents for verification and complete fee payment to confirm admission; some private universities may also consider merit-based admissions with interviews.

Course Syllabus

Semester 16 Subjects
Engineering Mathematics I
Programming for Problem Solving (C/Python)
Engineering Physics
Basic Electrical and Electronics Engineering
Engineering Graphics
Communication Skills
Semester 26 Subjects
Engineering Mathematics II
Data Structures
Engineering Chemistry/Environmental Science
Digital Logic Design
Object-Oriented Programming (Java/C++)
Workshop/Engineering Practices
Semester 36 Subjects
Discrete Mathematics
Design and Analysis of Algorithms
Computer Organization and Architecture
Database Management Systems
Probability and Statistics
Data Structures and Algorithms Lab
Semester 46 Subjects
Operating Systems
Computer Networks
Theory of Computation
Software Engineering
Machine Learning Fundamentals
DBMS/OS Lab
Semester 56 Subjects
Artificial Intelligence
Deep Learning
Natural Language Processing
Data Mining and Warehousing
Research Methodology and Technical Writing
AI/ML Lab
Semester 66 Subjects
Computer Vision
Big Data Analytics
Reinforcement Learning
Cloud Computing Fundamentals
Professional Elective I (Domain/Advanced ML)
Mini Project/Industrial Training
Semester 76 Subjects
MLOps and Model Deployment
AI Ethics and Responsible AI
Information Security Basics
Professional Elective II
Open Elective
Major Project Phase I
Semester 85 Subjects
Advanced Topics in AI/ML (Elective)
Industry Internship/Project
Major Project Phase II (Capstone)
Seminar/Comprehensive Viva
Entrepreneurship/Innovation Management

Career Options & Jobs

Graduates can work in IT services, product companies, analytics firms, and AI-focused startups, with roles spanning model development, applied research, and deployment. With strong projects and internships, students can progress to specialist roles in NLP, computer vision, or MLOps, and later to lead/architect positions or pursue M.Tech/MS/PhD.

Career Options

Machine Learning Engineer
Data Scientist
AI Engineer
Computer Vision Engineer
NLP Engineer

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