Build, Evaluate, and Apply Artificial Intelligence with These 5 Online Programs

Learning artificial intelligence today involves more than understanding how models work. Professionals are increasingly expected to build AI solutions, check whether they perform reliably, and decide where those systems can be used effectively.

That creates different learning needs. One person may want practical experience with machine learning, RAG, AI agents, and deployment. Another may need stronger mathematical foundations, deeper AI engineering knowledge, or a postgraduate degree that supports long-term technical growth.

These five online programs cover those needs from different angles, ranging from focused professional certificates to advanced degree pathways.

Overview: 5 Online Artificial Intelligence Programs

1. PG Program in Artificial Intelligence & Machine Learning – Great Learning

Great Learning’s aiml course takes learners from Python and machine learning into deep learning, NLP, Generative AI, RAG, Agentic AI, MLOps, and LLMOps. The emphasis is on using these ideas rather than studying them only at a conceptual level.

Delivery & Duration: Online for 12 months, with 24/7 learning resources and live weekend mentorship sessions.

Credentials: Certificates from the McCombs School of Business at The University of Texas at Austin and Great Lakes Executive Learning.

Program Highlights: 11+ hands-on projects, 60+ case studies, a four-week capstone, 38+ tools and technologies, GenAI, multimodal AI, agents, deployment, and career support.

Outcomes: Learners can build ML models, create RAG pipelines, develop single- and multi-agent workflows, and assess AI outputs, trade-offs, risks, and business value.

Why should you choose this course?

• The curriculum covers the AI workflow from modeling to deployment. ML and deep learning are followed by GenAI, agents, MLOps, and LLMOps.
• Practical work is spread across the program. Projects, cases, and the capstone provide repeated opportunities to apply what you’ve learned.

2. Advanced Certificate Program in AI, ML and DL – IIT Delhi

IIT Delhi’s six-month program is useful for learners who want to understand what happens behind an ML or deep learning system. It begins with Python, analytics, and applied mathematics before moving into neural networks and newer AI applications.

Delivery & Duration: Six months online, including 80 hours of live teaching, assignments, a three-week capstone, and optional campus immersion.

Credentials: Successful learners receive an e-certificate from CEP, IIT Delhi.

Program Highlights: ML and deep learning foundations, Keras, TensorFlow, RAG, Agentic AI evaluation, nine industry-focused tools, and a Bring Your Own Project capstone.

Outcomes: Participants will be able to train neural networks, evaluate AI approaches, and apply ML and deep learning methods to practical problems.

Why should you choose this course?

• It focuses on how models work. The curriculum moves from mathematics and Python into neural networks rather than treating AI as a ready-made tool.
• The capstone can be connected to a learner’s own problem. This gives professionals room to apply the course to relevant work.

3. Master of Applied Artificial Intelligence (Global) – Deakin University

The masters in ai offers a longer route for professionals who want practical AI training followed by advanced university study. The second year extends into areas that shorter programs may only touch briefly.

Delivery & Duration: Fully online over 24 months, beginning with a 12-month AI and ML pathway followed by 12 months of Deakin study.

Credentials: Master of Applied Artificial Intelligence (Global) from Deakin University, along with postgraduate certificates earned during the pathway.

Program Highlights: Reinforcement learning, computer vision, speech processing, robotics, human-aligned AI, mathematics for AI, GenAI, 11+ projects, 60+ case studies, and a capstone.

Outcomes: Graduates learn to design, build, evaluate, and deploy AI solutions while considering explainability, safety, ethics, and human requirements.

Why should you choose this course?

• Advanced AI areas receive dedicated study. Robotics, reinforcement learning, vision, speech, and human-aligned AI form part of the degree.
• It combines applied work with a university qualification. That can suit professionals looking beyond short-term skill development.

4. M.Tech. (Online) in Artificial Intelligence – IISc Bengaluru

IISc takes a more rigorous route. Its online M.Tech. is designed for employed engineers and combines coursework with a substantial project carried out inside the learner’s organization.

Delivery & Duration: Fully online synchronous classes, usually completed in 2-3 years, with evening and weekend scheduling.

Credentials: M.Tech. (Online) in Artificial Intelligence from the Indian Institute of Science.

Program Highlights: 16 core credits, 20 elective credits, 28 project credits, more than 30 elective choices across the online M.Tech. portfolio, and faculty-guided workplace research.

Outcomes: Learners strengthen mathematical and technical foundations while applying advanced AI work to a real organizational problem.

Why should you choose this course?

• The project carries substantial academic weight. It is completed within the employer organization, with guidance from both the company and IISc.
• This is built for experienced engineers. Its entry requirements and sponsored structure make it more specialized than a general upskilling course.

5. Agentic AI: From Concepts to Practice – IIIT Hyderabad

IIIT Hyderabad focuses specifically on engineering agentic systems. The course follows the full lifecycle, from architecture and reasoning to evaluation, deployment, monitoring, and AgentOps.

Delivery & Duration: Live online for 12 weeks, around 12 hours per week, with labs, assignments, recorded material, and a planned campus immersion.

Credentials: Professional certificate issued directly by IIIT Hyderabad.

Program Highlights: RAG, agent architecture, reasoning and planning loops, tool use, memory, MCP, A2A, multi-agent coordination, benchmarking, deployment, and maintainability.

Outcomes: Professionals learn to design agent systems, make architecture trade-offs, evaluate reliability, and operate agents in production-oriented settings.

Why should you choose this course?

• It concentrates on one fast-developing part of AI. The syllabus goes much deeper into agent architecture than a broad AI certificate can.
• Evaluation and operations start from the beginning. Learners study how agents behave after deployment, not only how to build them.

Conclusion

Building AI skills can mean different things depending on the role. Some professionals need faster exposure to models, GenAI, and deployment, while others may benefit from deeper engineering study, workplace research, or a full postgraduate degree.

When comparing ai courses, look at what you will actually build, how your work will be evaluated, and whether the curriculum reaches the technical areas you need. Duration and credentials matter, but the program should ultimately prepare you to apply AI responsibly to problems you expect to handle in practice.