Live flagship programme12 weekends · Live cohort + four-week capstone · 8–10 hours per weekView curriculum
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Flagship programme · AI & Machine Learning

Applied AI & ML Engineering Certification

Move from notebooks to reliable machine-learning and applied-AI systems.

PythonPandasScikit-learnPyTorchMLflowDocker
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12 weekends8–10 hours per weekLive cohort + four-week capstone
No payment on this pageProgramme guidance is captured for relevant follow-up
12 weekendsProgramme duration
Live cohort + four-week capstoneLearning format
8–10 hours per weekExpected effort
₹1,49,999Indicative fee

Demonstration contentPrices, commercial terms, mentor identities, testimonials and outcome claims in this starter build must be approved before launch.

Decision snapshot

Know who this is for and what changes after completion.

Designed for

  • Data professionals
  • Software engineers
  • Analysts
  • ML practitioners

Prerequisites

  • Python fundamentals
  • Basic statistics
  • Familiarity with data handling

Practical outcomes

  • Prepare data and build reproducible pipelines
  • Select and evaluate models against realistic objectives
  • Deploy models through reliable services
  • Monitor performance, drift and operational constraints
  • Present an end-to-end applied-AI capstone

Career relevance

Roles and capabilities this programme is designed to support.

Role outcomes depend on prior experience, project evidence, market conditions and interview performance.

Target roles

Applied AI EngineerMachine Learning EngineerAI/ML EngineerData ScientistMLOps-oriented Engineer

Tools and systems

PythonPandasScikit-learnPyTorchMLflowDockerCloud

Curriculum

A structured path from foundations to production evidence.

Modules are presented at decision level; the final syllabus should be governed through the course CMS.

Module 1

Data and experimentation foundations

  • Data quality
  • Feature workflows
  • Experiment design
  • Reproducibility
Module 2

Machine-learning systems

  • Supervised learning
  • Evaluation
  • Error analysis
  • Model selection
Module 3

Deep learning and applied AI

  • Neural networks
  • Transfer learning
  • NLP and vision patterns
  • Responsible evaluation
Module 4

Deployment and MLOps

  • Serving
  • Versioning
  • Pipelines
  • Monitoring
Module 5

Production constraints

  • Latency
  • Cost
  • Reliability
  • Data and model governance
Capstone

End-to-end AI/ML system

  • Problem framing
  • Build
  • Deploy
  • Monitor and present

Projects and capstone

Build evidence that can be reviewed, explained and improved.

Projects should make decisions, trade-offs, tests and operating context visible.

01

Data-quality and feature pipeline

Define the problem, build the artefact, document decisions and review production readiness.

02

Model evaluation study

Define the problem, build the artefact, document decisions and review production readiness.

03

Deployed inference service

Define the problem, build the artefact, document decisions and review production readiness.

04

Applied AI/ML capstone

Define the problem, build the artefact, document decisions and review production readiness.

Learning support

Support is designed around completion, evidence and readiness—not passive attendance.

Live expert instruction
Labs and project review
Portfolio and interview preparation
Eligible placement support

Experts

Sample mentor profiles

Meet mentors

Learner evidence

Concise proof without turning the page into a testimonial wall.

Replace each sample capsule with an approved name, designation, photo and outcome-backed quote.

Verified learner storyApplied AI & ML learner
Sample

Replace this sample capsule with a verified learner quote and approved photograph before production launch.

Verified learner storyWorking professional
Sample

This component supports a concise quote, designation and optional photo without turning the page into a long testimonial wall.

Role visibility

Related sample jobs

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Sample listing

Data Engineer

Orbit Fintech

MumbaiHybrid3–6 years
PythonSQLSparkData Pipelines
₹18L–₹28LView role

Programme thinking

Course-specific articles

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Fees and financing

Indicative programme fee: ₹1,49,999

Use this section for approved enrolment amount, payment schedule, financing partners, refund terms and taxes. Commercial values in this build are placeholders until signed off.

View fees and financingRead refund policy
  • Transparent total fee and taxes
  • Approved 0% EMI or financing terms
  • Written refund and cancellation terms
  • No placement guarantee language

Questions

Applied AI & ML Engineering FAQs

Concise answers for the decision context of this page.

Is this programme suitable for working professionals?

Yes. The programme format is designed around structured live sessions, guided practice and planned project work. The exact weekly commitment is shown on the programme page.

Do I need prior experience?

Prerequisites differ by track. Foundational programmes accept earlier-stage learners, while advanced and leadership tracks expect relevant engineering experience.

How are learners assessed?

Assessment can include practical reviews, live problem-solving, project milestones, mock interviews and a capstone.

Does the programme include placement support?

Eligible learners receive the services described on the placement-support page. Placement support is not a job guarantee and depends on readiness, role fit and employer requirements.

Can I pay in instalments?

Financing and instalment options can be configured for each cohort. Final terms should be confirmed during admission.

Programme guidance

Decide whether Applied AI & ML Engineering fits your next role.

Share your experience, target role and learning objective. The form can be connected to the production CRM endpoint.

Your information is used only to respond to this request and manage relevant follow-up.

When no production endpoint is configured, this preview stores a demo submission in the browser only.

Related events

Learn with a live context around this pathway.

Events connect the curriculum with practitioners, hiring conversations and current role expectations.

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