An engineering-first program that takes you from Python fundamentals to production machine learning. Supervised and unsupervised learning, deep learning, computer vision, natural language processing and MLOps — taught through hands-on learning, industry case studies and real client projects, then a 3-month industry internship inside a working delivery team.
The AI & Machine Learning Program is a 12-Month Career Program for students and freshers who want to build AI systems rather than describe them. It's deliberately engineering-first: every concept is introduced through a problem, implemented in code, evaluated honestly, and then deployed somewhere it can break.
You begin with Python, data handling and the mathematics that machine learning actually depends on — linear algebra, calculus intuition, probability and optimisation, each taught in service of a model you're about to build. From there the program moves through the classical ML canon, then into deep learning with PyTorch, computer vision, natural language processing and transformer architectures.
What most programs skip, this one treats as core: MLOps. Experiment tracking, model registries, containerised serving, CI/CD, monitoring and drift detection. A model that never leaves a notebook has no commercial value, and hiring managers know it — so we make deployment a graded outcome, not an optional extra.
Generative AI runs alongside as a working layer — fine-tuning, retrieval-augmented generation and evaluation — giving you future-ready skills across both classical ML and modern foundation models. The program closes with a 3-month industry internship on real client projects under industry mentorship, wrapped in placement preparation and 100% job assistance.
One continuous career-focused track. You don't finish a course and then go hunting for experience — the industry internship is built into the program.
Live instructor-led sessions covering Python, ML mathematics, classical machine learning, deep learning, computer vision, NLP, generative AI and MLOps — each module closing with a working, reviewed implementation.
You join a delivery team on live client AI problems — scoping, training, evaluating and deploying models — with weekly mentor code reviews and stakeholder demos.
Three connected tracks: the mathematical and programming foundation, the modelling core from classical ML to deep learning, and the engineering layer that gets it into production.
Practical learning throughout: each module is anchored to a real business scenario and ends in a deliverable. Every tool is taught the way it's used on the job — not in isolation.
The programming foundation, written to engineering standards from week one — because your internship mentor will read this code.
Only the maths you'll actually use — introduced through the model that needs it.
Models are downstream of data. Learn to build the upstream properly.
The models that still solve most commercial problems — implemented, tuned and evaluated.
Finding structure in data nobody has labelled.
Neural networks built from the mechanics up, so you can debug what you deploy.
Teaching machines to see — from convolutions to a deployed detector.
From tokenisation to transformers, the full modern NLP arc.
Working with models you didn't train — the dominant pattern in industry today.
The module that makes you employable rather than merely knowledgeable.
The questions every enterprise AI review board will ask you.
End-to-end AI delivery on a real client problem, under industry mentor supervision.
Concrete, demonstrable capabilities built through practical learning — the kind you can show in a portfolio and defend in an interview.
Build core algorithms yourself before reaching for a library — so you can diagnose them when they misbehave in production.
Structure PyTorch training loops, tune optimisers and schedulers, and recognise the failure modes behind a flat loss curve.
Design evaluation that survives contact with reality — catching leakage, imbalance, distribution shift and metric gaming.
Fine-tune, retrieve, prompt and evaluate LLMs, choosing between adaptation and retrieval on cost and quality grounds.
Containerise and serve a model, track experiments, wire CI/CD and monitor for drift once real traffic arrives.
Measure bias, explain predictions, and document a model well enough to pass an enterprise governance review.
The final 3 months are an industry internship on live client AI problems — real data, real constraints, real code review.
You're assigned live briefs from Ergebins Technologies' delivery pipeline and partner organisations — messy data, hard deadlines, actual stakeholders.
A practising ML engineer reviews your code, architecture choices and evaluation weekly, at the standard a senior would apply on a real team.
On completion you receive an Internship Certificate and a Project Experience Letter — verifiable experience for your résumé and background checks.
Portfolio development is built into the program. You leave with work you can walk a hiring manager through, line by line.
Build an interpretable credit default model on imbalanced lending data, calibrate probabilities, and produce the explainability report a risk committee would demand.
Train a computer vision model to spot manufacturing defects from image data, handle scarce positive samples, and deploy it behind a real-time inference API.
Classify and route incoming support tickets automatically, benchmark classical models against transformers, and quantify the hours saved.
Build collaborative and content-based recommenders, evaluate with ranking metrics, and address the cold-start problem head on.
Ship a monitored, containerised model service end to end: training pipeline, registry, CI/CD, drift monitoring and a rollback plan.
Built for students and freshers who enjoy building things. A computer science degree helps but is genuinely not required.
Certificates are awarded on assessed performance — completed modules, reviewed projects and a finished industry internship.
Awarded on assessed completion of all twelve modules, graded implementations and your capstone project — issued by DICA, the education brand of Ergebins Technologies Private Limited.
Issued on completion of the 3-month industry internship, confirming the client engagement, your role and the duration of the placement.
A verifiable letter detailing the live client projects you delivered, the stack you used and the outcomes achieved.
Common roles our graduates step into. Final compensation depends on background, performance and location.
Own models end to end — training pipelines, serving infrastructure, monitoring and retraining.
₹8 – 20 LPABuild applied AI features into products, combining classical models with foundation models where each fits best.
₹9 – 22 LPADesign and deploy vision systems for inspection, detection, tracking and document understanding.
₹8 – 20 LPABuild language systems — classification, extraction, retrieval and generation — and the evaluation that keeps them honest.
₹8 – 20 LPAOwn the infrastructure that lets a team ship models reliably: pipelines, registries, CI/CD and observability.
₹9 – 22 LPAFrame problems statistically, build and validate models, and translate results into business decisions.
₹8 – 18 LPAPlacement preparation starts in month seven, runs through your internship, and continues until you accept an offer.
One-on-one resume rewrites tuned to the roles you're targeting, plus a reviewed portfolio of your live projects, dashboards and capstone work.
HR, aptitude and technical mock interviews with recorded feedback. You rehearse the real rounds before you sit in them.
Business communication, stakeholder storytelling, group discussions and presentation practice — taught to corporate training standards.
Profile shortlisting, referrals into our hiring partner network, interview scheduling and continued career support until you accept an offer.
Placement preparation, mock interviews, and referrals into a recruiter network that hires for analytics, data science, and AI roles.
Company names and marks belong to their respective owners.
Outcomes from recent cohorts — live projects, internships and offers.
The MLOps module is the reason I got hired. Every other candidate had a notebook; I had a containerised service with monitoring and a rollback plan.
Implementing gradient descent by hand before touching scikit-learn felt slow at the time. In interviews it was the difference between explaining a model and reciting one.
I came from a mechanical engineering background with no coding. The defect detection project during the internship became the centrepiece of my portfolio.
The program is 9 months of learning + 3 months of industry internship = 12 months in total.
Both are 12-Month Career Programs and they overlap on Python and modelling. Data Science leans toward statistics, experimentation and business decision-making; AI & Machine Learning leans toward engineering — deeper on deep learning, computer vision, NLP and MLOps. If you want to build and deploy systems, choose this one. A counsellor can help you decide.
No. Roughly a third of each cohort comes from non-CS backgrounds. Module one starts at core Python and module two rebuilds the required mathematics from fundamentals.
Yes. The 3-month industry internship is included in the 12-Month Career Program at no additional cost. You work on real client projects and receive an Internship Certificate and Project Experience Letter on completion.
You'll deploy. Module ten covers containerised serving, experiment tracking, CI/CD and monitoring, and your capstone project is assessed on a running, monitored service — not on a notebook.
A laptop with 8 GB RAM is enough. Compute-heavy training runs use cloud notebooks and GPU resources provided as part of the program, so you don't need your own GPU.
Resume building, LinkedIn and portfolio development, GitHub review, mock interviews across HR, aptitude and technical rounds, soft skills training, hiring drive access, and referrals into our hiring partner network — including Technify, Infosys, LTIMindtree, EY, Capgemini, Grail Consultant, Intellicia and Thread Security.
Yes. We accept UPI, credit and debit cards, Visa and Mastercard, and offer No Cost EMI on selected cards and partner lenders, subject to eligibility and approval.
Share your details and a career advisor will call you back within one working day with the full curriculum, batch dates, fee structure and No Cost EMI options.
Book a free 15-minute career counseling session and reserve your seat before applications close.