Best AI Courses in India (2026): Fees, Eligibility and Curriculum Compared

Of everything I looked at, this is the program that most directly takes you from learning AI to being hired for it. It's built for working professionals and career switchers who want live training, practical projects and placement support in one place. It is also our own program, so weigh this review with that in mind.

Seven months of live online classes on weekends (9 AM–12 PM IST), with live doubt sessions on weekdays and recordings of every class. The sequence runs Python, NumPy, pandas, statistics and linear algebra → classical ML → deep learning (CNNs, sequence models, Transformers) → NLP → LLM fundamentals and prompt engineering → RAG with LangChain and vector databases → fine-tuning with LoRA/QLoRA → Agentic AI (tool use, planning, memory, multi-agent orchestration) → MLOps (containers, API serving, CI/CD, monitoring), plus DSA and ML system-design practice for interviews.

It doesn't win on any single feature. It ranks first because the design decisions line up for a career move: live classes for accountability, a curriculum that reaches RAG, fine-tuning and agents, deployed projects rather than notebooks, and placement support that continues after the course. It's also the only course under ₹1 lakh on this list that combines live teaching with placement support.

What I'd push back on. It's a fixed seven-month weekend commitment. It isn't a university degree. The course page states the programming prerequisite two different ways, so ask admissions whether your batch expects basic coding. And under the published refund policy, discounted enrolments (the ₹74,000 price) aren't refundable, so be sure about your schedule before you pay the discounted fee.

Anyone who needs a fully self-paced course or a formal academic degree, and anyone who only wants one niche skill. For edge AI, HarvardX TinyML is the better fit. For a short technical LLM course, DeepLearning.AI is enough.

Andrew Ng's team and AWS explain how LLMs are trained, adapted and evaluated more clearly than anything else at this price. It's a short technical course for people who already code, not a job program.

A single self-paced Coursera course of about 13–17 hours. It covers how Transformer-based LLMs work, the generative AI project lifecycle (choosing, adapting, evaluating and deploying a model), prompt engineering and in-context learning, full and parameter-efficient fine-tuning (PEFT/LoRA), reinforcement learning from human feedback, and how LLMs connect to external data and applications. Each week has a graded Python lab.

As a technical primer on LLMs it's excellent value. The labs make you fine-tune a real model, which few low-cost courses do.

The trade-off is scope and level. It's short, it assumes Python and basic ML, and retrieval-augmented generation and agents only appear briefly in lectures. There's no live instructor, mentoring or placement support. Finish it, then build one or two projects of your own, such as a RAG app, so you have something to show in interviews.

Complete beginners and non-technical learners (start with Generative AI for Everyone instead), and anyone who needs live teaching, mentoring or placement support.

The only master's degree on this list. It's a top-up degree for people who have already completed IIIT Bangalore's Executive PG Programme in ML & AI.

An 18-month programme combining recorded videos and live sessions, taught by IIIT Bangalore faculty, with the degree awarded by Liverpool John Moores University (UK). The curriculum builds on the IIIT-B diploma with advanced ML, deep learning, NLP and research methods, and ends with an industry capstone and research project.

If your employer, a promotion rule or a visa or further-study plan needs a master's degree, this is the degree route on this list, and the IIIT-B teaching brings academic rigour. Our scorecard rates it Strong or Deep on classical ML and deep learning.

Check two things first. Eligibility: you need IIIT-B's Executive PG Programme in ML & AI and a year of technical work experience before you can enrol, so for most people this is the second of two programmes. Cost and focus: ₹5,35,000 or more over 18 months, with GenAI present but not central (prompt engineering and RAG rated Basic; fine-tuning and agents Limited).

Anyone who hasn't completed the IIIT-B diploma, anyone planning to switch roles within the next year, and anyone whose main goal is building GenAI applications rather than earning a degree.

The strongest institute brand on this list for working STEM graduates. It's a six-month IIT Hyderabad certification, not a degree.

A six-month programme with 200 hours of live weekend classes and 60 hours of labs, taught with IIT Hyderabad faculty, plus a two-day campus visit at IIT Hyderabad. It covers the mathematical foundations of ML, supervised and unsupervised learning, deep learning and computer vision, NLP and Transformers, and an introduction to Generative AI, ending with a capstone.

The IIT Hyderabad name and faculty bring rigour that holds up. Our scorecard rates it Deep on deep learning and computer vision and Strong on classical ML. At about ten hours a week, it fits around a full-time job.

Set expectations on three points. GenAI is moderate (prompt engineering and RAG rated Basic; fine-tuning and agents Limited). Dedicated placement support is limited, so plan your own job search. And the fee is non-refundable and non-transferable, so be sure about your schedule before you pay.

Non-STEM graduates (ineligible), anyone targeting a GenAI- or agent-heavy role, anyone who can't commit to weekend classes and the campus visit, and anyone looking for dedicated placement support.

A university-branded live program that we could not reconfirm this month. Read the note below before you rely on this entry.

As previously listed: eleven months of live online classes covering Python, ML with TensorFlow, deep learning, Generative AI and prompt engineering, Agentic AI and MLOps, with IBM Watsonx labs, 15+ projects, 3 capstones and monthly Purdue masterclasses, aimed at people with 2+ years of experience.

When we last reviewed it, this was one of the more current university-branded programs. Our scorecard rated it Good across most 2026 rows, with RAG and fine-tuning at Moderate. The Purdue + IBM certification travels well for MNC roles.

On 24 October 2026 the Purdue programme pages redirected to Simplilearn's general AI catalogue, and we found no current INR price. Until Simplilearn publishes a page for it, treat the details above as unconfirmed. The closest live program we could confirm is Simplilearn's PG Program in AI & ML with I-DAPT, IIT (BHU): bachelor's degree, basic programming and maths, 8 months, 6–9 hours a week, Saturday–Sunday 9:00 AM–1:00 PM IST, ₹1,40,000 including taxes. We have not reviewed that program.

Anyone who needs a confirmed fee and schedule today, complete beginners without basic coding or maths, and anyone who needs deep RAG, fine-tuning or multi-agent work.

A mentorship-led program with a UT Austin certificate, and the most accessible structured option here for graduates who have never coded.

Twelve months of recorded lectures with two hours of weekend live mentorship, starting with a one-month Python bootcamp. It covers Python and data science foundations, supervised and unsupervised ML, deep learning, computer vision, NLP, Generative AI for business use cases, and MLOps and cloud deployment, with projects and case studies.

Its distinctive strengths are accessibility and MLOps. No prior programming is required, and deployment, monitoring and cloud platforms get more time than in most programs here (our scorecard rates MLOps Deep). The UT Austin certificate stands out on a resume.

Where I'd manage expectations: most teaching is recorded, with live time concentrated in weekend mentor sessions. Fine-tuning and agents are rated Limited, and GST is charged on top of the ₹2,75,000 fee. A median salary hike is provider-reported, but a placement rate is not published.

Anyone targeting agentic AI or fine-tuning work, anyone who wants mostly live teaching, and anyone without a bachelor's degree (ineligible).

Udacity turns the usual model around: you build, submit, get a human code review, and revise.

About 52 hours of self-paced content covering Python programming for AI, NumPy, pandas and Matplotlib, the linear algebra and calculus behind neural networks, and deep learning with PyTorch. The centrepiece is an image classifier built end to end, and each project submission gets expert code review.

The project-first model with line-by-line code review is something almost no other self-paced option here offers. You finish with portfolio-ready work and solid PyTorch basics.

Two honest limitations. Accountability: there's no live cohort, so it suits self-directed learners. Scope: it's an AI programming foundation; RAG, fine-tuning, agents and MLOps are rated Limited or Basic, and there's no placement program.

Anyone who has abandoned a self-paced course before, and anyone who needs placement support or GenAI application depth.

The cheapest enterprise-branded AI certificate on this list, for people who can already program in Python.

A self-paced Coursera certificate at about 10 hours a week for 4 months. It covers machine learning with Python, deep learning with Keras and PyTorch, building and deploying AI applications, and Generative AI and LLM basics, ending with a capstone project and an IBM digital badge.

The IBM name carries weight in enterprise settings, the badge is shareable on LinkedIn, and the deployment focus is useful. Our scorecard rates it Good or Moderate across most rows.

It isn't a beginner course. Coursera lists it as intermediate, expects working Python, and IBM recommends its Data Science or Applied AI certificate first. There's no live mentoring or placement team, and agents are rated Basic. Pair it with two or three self-built, deployed projects.

Non-programmers and complete beginners, anyone who needs accountability or mentoring, and anyone preparing specifically for GenAI-specialist interviews.

A specialist course, not a general AI career program: you shrink deep learning models until they run on microcontrollers.

Three self-paced edX courses at 2–4 hours a week, free to audit with a paid certificate track. They cover the fundamentals of TinyML, applications such as keyword spotting, visual wake words and anomaly detection, model optimisation through quantisation, and deployment with TensorFlow Lite for Microcontrollers on the TinyML Program Kit, plus responsible AI for embedded devices.

For IoT and edge computing, this is world-class teaching you won't find elsewhere on this list, and the hands-on hardware work is a real differentiator.

Judge it against its own goal. It doesn't cover LLMs, RAG or agents, has no career support, and the deployment course needs a hardware kit and basic C/C++. That's why it ranks ninth for general AI careers despite a high editorial score: it's excellent at a niche.

Anyone targeting mainstream AI Engineer, Data Scientist or GenAI roles, and anyone who needs career support.

The best free resource for programmers who want to learn deep learning by building it.

Nine lessons of about 90 minutes each, free and self-paced. You train an image classifier with transfer learning in the first lesson, deploy models as simple web apps, and then work back to neural network foundations (SGD, loss functions, backpropagation), NLP with Transformers and Hugging Face, tabular modelling, collaborative filtering and data ethics. All materials are open source.

Jeremy Howard's top-down approach is highly effective. Our scorecard rates it Deep on deep learning and computer vision, and Good on fine-tuning.

It ranks tenth because the ranking measures fit for an India-based career move, not teaching quality alone. It expects about a year of coding, gives no certificate, career support or 1:1 help, and doesn't cover RAG, agents or LLM application design. As a free complement to a structured program, it's outstanding.

Non-programmers, anyone who needs a certificate, accountability or career support, and anyone whose goal is building LLM applications or agents.