What Master's courses in Artificial Intelligence or Machine Learning should I consider, given my background in B.Tech CSE (AI and ML) and over 2 years of work experience?

Your specific undergraduate specialisation in AI and ML already exceeds what most CSE master's applicants bring, so at over 2 years of work experience the real filter is whether a programme wants a narrow, research-grade ML degree or a broader AI degree covering NLP, computer vision and robotics as well.

Programmes that reward your exact background

• UCL, MSc Machine Learning: requires an upper-second-class-equivalent degree in a highly quantitative subject like CS, mathematics or electrical engineering, runs for one calendar year, and links to the Gatsby Computational Neuroscience Unit and Google DeepMind.
• University of Edinburgh, MSc Artificial Intelligence: unusually broad, covering Applied ML, NLP, Computer Vision, Probabilistic Modelling and Robotics, and explicitly accepts backgrounds in computer science and AI.
• University of Manchester, MSc Machine Learning: covers deep neural networks and causality with both theory and practice, a strong alternative if UCL's entry bar is out of reach.

Why your undergraduate transcript still matters at this stage

Your 28 months of work experience strengthens your application narrative, but for technical ML programmes like UCL's and ETH Zurich's, admission is still evaluated primarily on your bachelor's transcript and subject-specific prerequisites, so do not assume experience alone offsets a weaker undergraduate grade.

This Information Is Also Available On

University College London, University of Edinburgh and University of Manchester's own postgraduate course pages set out the entry requirements referenced above.

My Advice

If you have not decided between core ML research and broader AI, choose Edinburgh over UCL, since Edinburgh's structure lets you sample NLP, vision and robotics before committing, while UCL's is deliberately narrow and best suited to someone already certain they want ML engineering or research specifically.