Emerging Technology
AI & Machine Learning Courses: Study Routes & Eligibility
Artificial Intelligence (AI) and Machine Learning (ML) focus on building computer systems that can work with data, identify patterns and perform tasks that normally require intelligent decision-making.
Students interested in programming, mathematics and data can compare engineering, postgraduate and focused AI/ML skill routes here. This guide explains eligibility, learning areas and further-study options.
Last reviewed: 2026
AI & ML at a Glance
- Field
- Artificial Intelligence & Machine Learning
- Study Area
- Computer Science & Emerging Technology
- Popular Study Routes
- B.Tech CSE (AI & ML), B.Tech AI & ML and related programmes
- Learning Areas
- Programming, Data, Algorithms, Machine Learning & AI
- Admission
- Depends on selected programme and institution
- Career Areas
- AI, Machine Learning, Data, Software & Automation
Course names, eligibility and curriculum vary by institution.
AI & Machine Learning Course Overview
Artificial Intelligence enables computer systems to perform intelligent tasks. Machine Learning is an area of AI that learns patterns from data.
AI & ML programmes generally bring together computing fundamentals, programming, mathematics, data and specialized AI subjects.
Popular AI & ML Courses
| Course | Level | Typical Focus |
|---|---|---|
| B.Tech CSE (AI & ML) | Undergraduate | Computer Science with AI and Machine Learning specialization |
| B.Tech Artificial Intelligence & Machine Learning | Undergraduate | Engineering-focused AI, ML and computing |
| B.Tech Artificial Intelligence & Data Science | Undergraduate | AI, data science and computing fundamentals |
| M.Tech Artificial Intelligence | Postgraduate | Advanced AI and related technical study |
| M.Tech AI & ML | Postgraduate | Advanced Machine Learning and AI concepts |
| Certificate / Skill Programmes | Varies | Focused AI/ML skill development |
On smaller screens, scroll the table to see all columns.
Programme names and curriculum vary by institution. For broader route choices, compare computer science study routes.
AI & ML Course Eligibility
Undergraduate routes such as B.Tech AI & ML or B.Tech CSE (AI & ML) require the applicable engineering admission qualifications of the institution or admission authority.
Postgraduate programmes generally require an appropriate qualifying degree specified by the institution. Entry requirements for skill programmes can differ.
Always verify current course-specific eligibility, required subjects, entrance requirements and admission rules before applying.
Why Study AI & Machine Learning?
Emerging Technology Learning
Understand AI concepts and their applications.
Programming & Problem Solving
Build computational approaches to practical problems.
Data-Driven Skills
Learn to work with data and evaluate models.
Multiple Technology Applications
Explore intelligent systems across different domains.
Higher Study & Research Opportunities
Consider advanced study where qualification requirements are met.
AI & ML Admission Step by Step
Choose the Right AI & ML Programme
Check Eligibility
Shortlist Colleges / Universities
Complete Applicable Entrance or Admission Process
Document Verification & Admission Confirmation
The exact process varies by programme and institution. For help with application and document requirements, discuss your admission process.
Popular AI & ML Specializations
| Area | Main Focus |
|---|---|
| Machine Learning | Learning patterns and predictions from data |
| Deep Learning | Neural-network-based learning methods |
| Natural Language Processing | Working with human language and text |
| Computer Vision | Understanding and processing visual information |
| Data Science | Data analysis, modelling and insights |
| Robotics & Intelligent Systems | AI applications in automated systems |
| Generative AI | Systems that generate text, images, code or other content |
| AI for Automation | Applying intelligent systems to automated processes |
On smaller screens, scroll the table to see all columns.
Specialization availability varies by programme and institution.
AI & ML Subjects and Syllabus
| Learning Area | Typical Topics |
|---|---|
| Programming | Python and programming fundamentals |
| Mathematics | Linear algebra, probability and statistics |
| Data Structures & Algorithms | Computational problem solving |
| Database Systems | Data storage and management |
| Machine Learning | Models, training and evaluation fundamentals |
| Artificial Intelligence | AI concepts and intelligent systems |
| Deep Learning | Neural networks and advanced learning concepts |
| Data Science | Data analysis and modelling |
| AI Applications | NLP, computer vision or related topics, depending on curriculum |
On smaller screens, scroll the table to see all columns.
Exact syllabus varies by university and programme.
Programming & Tools Used in AI & ML
- Python
- SQL
- Jupyter
- NumPy
- Pandas
- Scikit-learn
- TensorFlow
- PyTorch
The programming languages, libraries and tools taught depend on the curriculum; this list is illustrative.
Skills for AI & ML Students
- Programming
- Mathematics & Statistics
- Problem Solving
- Data Analysis
- Machine Learning Fundamentals
- Algorithms
- Logical Thinking
- Continuous Learning
Career Options After AI & ML
Opportunities depend on qualification, technical skills, projects, specialization, experience and employer requirements.
| Career Area | Example Roles |
|---|---|
| Artificial Intelligence | AI-related engineering/development roles |
| Machine Learning | Machine Learning-related technical roles |
| Data Science | Data Analyst / Data Science-related roles |
| Software Development | Software development roles |
| NLP | Language technology-related roles |
| Computer Vision | Image/video intelligence-related roles |
| Automation | AI and automation-related technical roles |
| Research | Research-oriented roles where qualification requirements are met |
On smaller screens, scroll the table to see all columns.
Role titles and eligibility vary by employer.
AI & ML Salary in India
Compensation varies by qualification, technical skills, role, employer, location and professional experience.
| Factor | Impact |
|---|---|
| Technical Skills | Role suitability can vary |
| Projects | Practical experience may strengthen a profile |
| Specialization | Different AI domains have different requirements |
| Experience | Professional experience affects opportunities |
| Employer | Compensation structures vary |
| Location | Market conditions differ |
On smaller screens, scroll the table to see all columns.
Higher Study Options in AI & ML
| Starting Programme | Possible Further Study |
|---|---|
| B.Tech AI & ML | M.Tech AI / ML / Data Science or other eligible postgraduate programmes |
| B.Tech CSE (AI & ML) | M.Tech CSE / AI / related postgraduate study |
| Relevant Master’s Degree | Research / doctoral programmes where eligible |
On smaller screens, scroll the table to see all columns.
Eligibility depends on the institution and programme.
Scope of AI & Machine Learning
AI and ML applications span software, finance, healthcare, manufacturing, education, e-commerce and cybersecurity. Generative AI, computer vision, language processing, data science and intelligent automation offer different areas for study and application.
Career outcomes depend on education, skills, projects, experience and market requirements.