Flagship program · Self-paced · Start any day
From your first line of Python to shipping production AI: LLM apps, RAG, evaluations, agents and fine-tuning. You write and test the code in every lesson.
₹56,999 ₹14,999 74% off7-day money-back promise
This program was built for these exact gaps. Every lesson makes you write the code yourself, and checks it.
It's probably not for you if…
Real screens from the learning app. Lesson 1.1 is free, so you can try every one of them before you pay.
Notes
Every lesson starts with where the idea shows up in real AI work, then builds it up one step at a time with worked examples. Lesson 3.8 alone is a 70-minute read on gradient descent, from a single step to mini-batches and learning-rate tuning.
Labs
Python runs inside your browser, so there is nothing to install. Each lab cell has checks that turn green when your code is right, and a finished lab gives you a personal completion code.
Practice path
Every lesson has three to six short sessions: put code lines in order, predict the output, fill the gap, spot the bug, match terms. Nova, your practice buddy, keeps score. Questions you miss come back after one day, then three, then seven.
Progress
Every lab you pass, exam you clear and project you ship is recorded. Every certificate carries a public ID that anyone can verify online.
Pick the one closest to you. Everyone starts with Module 1, and moves faster through what they already know.
You already write code. You'll add ML fundamentals, LLM APIs, RAG, evals and deployment, and skip ahead through Module 2 quickly.
You know SQL and spreadsheets. Python, ML and LLM apps turn your analysis into products other people use.
Start from zero and graduate with twelve public projects behind you.
Your systems knowledge matters in production AI: serving, monitoring and incident runbooks in Module 12.
Learn enough engineering to prototype, evaluate and talk to AI teams as a peer.
A complete path from your first script to deployed AI services.
Write, test and package Python like a professional, with Git and the command line.
Vectors, gradients, probability, SQL and scikit-learn, with every metric explained.
Train networks in PyTorch and build a mini-GPT so LLMs stop being a black box.
Structured outputs, retrieval, evaluation suites, MCP tools and human approval.
LoRA fine-tuning, Docker, CI/CD with eval gates, monitoring and runbooks.
Foundations: Modules 1–3
Advanced: Modules 4–13
13 modules, in order. Each one ends with something you can show.
Outcome. Git, APIs, environments and the vocabulary of AI: the tools everything else uses.
Project. No portfolio project: this module sets up the toolkit every later project uses.
Outcome. Python to a professional standard, ending in a working command-line capstone.
Project. Sales-analytics command-line tool. A real CLI with argparse, tests and exit codes.
Outcome. The vectors, derivatives and probability that make machine learning make sense.
Project. Math-to-ML notebook. A model trained by hand in NumPy, every step explained.
Outcome. Query real business data with SQL, build and honestly evaluate classical ML models, and explain every metric to a manager.
Project. Credit-risk or churn model on an Indian-style dataset: SQL feature pipeline, three models compared, cost-based threshold, model card. Published as a Hugging Face Space.
Outcome. Train neural networks in PyTorch and build a working mini-GPT, so LLMs stop being a black box.
Project. A from-scratch mini-GPT trained in Colab plus a sentence-embedding semantic search demo, with a write-up of what each component does.
Outcome. Build and ship an LLM-powered service that returns reliable, structured results, with cost and latency under control.
Project. Invoice-to-data service: upload GST invoices (image or PDF), get validated JSON, with a FastAPI backend, a simple UI, cost per document measured, deployed on a free tier.
Outcome. Use coding agents to build faster without shipping bugs: write specs, tests and reviews that keep AI-written code correct.
Project. Ship a real feature or fix to an open-source repository as a reviewed pull request, built with a coding agent and covered by tests.
Outcome. Build retrieval systems that answer from company documents with citations, in English and Indian languages, and prove how often they retrieve the right source.
Project. Bilingual policy assistant: answers questions about public Indian government documents in English and Hindi with citations, plus a retrieval-quality report.
Outcome. Measure whether an AI system is actually good, catch regressions before users do, and defend it against prompt injection and data leaks.
Project. A published evaluation suite and red-team report for the Module 8 assistant, wired into CI so a regression fails the build.
Outcome. Build agents that use tools, remember context, ask a human before risky steps, and plug into real software through MCP.
Project. Finance-operations agent: reconciles invoices against a ledger through MCP tools, asks for approval before any write, and logs every step.
Outcome. Decide when fine-tuning beats prompting, fine-tune a small open model on free GPUs, and serve it efficiently.
Project. Fine-tune a small open model to extract fields from Indian documents, beat the prompt-only baseline on a held-out eval set, and publish it on the Hugging Face Hub.
Outcome. Package, deploy, monitor and pay for AI systems like a production team does.
Project. Productionise the Module 10 agent: containerised, CI/CD with eval gates, deployed, with a monitoring dashboard and a written runbook.
Outcome. Deliver an AI project for a real problem end to end, and walk into interviews with proof: portfolio, JD-matched resume and practised answers.
Project. Client-style capstone for a small business, NGO or open dataset of the learner's choice: spec, build, evals, deployment, demo video and case study.
Each one lives on GitHub, with a README a recruiter can read in a minute.
Module 2
A real CLI with argparse, tests and exit codes.
Module 3
A model trained by hand in NumPy, every step explained.
Module 4
SQL features, three models, a cost-based threshold and a model card.
Module 5
A small transformer trained on Indian-language text.
Module 6
Images and PDFs in, validated JSON out, through FastAPI.
Module 7
A real feature shipped with a coding agent and tests.
Module 8
RAG over Indian government documents in English and Hindi, with citations.
Module 9
Regression tests for an AI app, wired into CI.
Module 10
Reconciles invoices through MCP tools, with human approval.
Module 11
Beats the prompt baseline; published on Hugging Face.
Module 12
Docker, CI/CD with eval gates, monitoring and a runbook.
Module 13
A real problem for a small business or NGO, end to end.
Self-paced means it fits around a job, college or a career switch. Pick who you are and how many hours you have, and see where you'd be each week.
Hours are the guided hours for every module, including those still to be published. Your pace will vary.
Plan your pace
I am a
Evenings and weekends. Start with Module 1's tools and use the labs on problems from your own job.
This program is new. We will only publish reviews from real learners, with their full name and their written permission. No paid, edited or AI-written reviews, ever. Until then, the most honest review is your own: Lesson 1.1 is free, with no card needed.
Every lesson, every module and the full program earn a certificate with a public verification ID.
A certificate of completion. It is not a degree and is not UGC or AICTE recognised.
Everything in the program, for one price. Try Lesson 1.1 free first.
We turn three this year. To celebrate three years of teaching AI, the complete program is at its anniversary price.
The AI Engineering Program
One price for all 13 modules and every update to them.
₹14,999
₹56,999 74% off
7-day money-back promise. Not for you? Ask within 7 days, with under 20% completed, and get a full refund.
Everything that's included
Learn
Practise and prove
Keep
Not included
If your company has a learning or training budget, this program may qualify. Send your manager this page: the curriculum, hours, projects and price are all on it. Or start from our email below.
Subject: Request to use my learning budget for AI engineering training Hi [manager's name], I'd like to take the The AI Engineering Program by Synaptova and would like to ask whether our learning budget can cover it. What it is: a self-paced online program covering Python, maths for machine learning, machine learning, LLM applications, retrieval (RAG), evaluations, agents and deployment. 13 modules and 346 guided hours in total. How I'd fit it in: about [N] hours a week, outside my core working hours. What it would help with here: [one or two problems in our team this would help me solve]. Proof of work: each module ends with a project, and every certificate has a public verification ID. Price: ₹14,999. The full curriculum, with hours and projects for every module, is here: https://synaptova.com/programs/ai-engineering/ Thanks, [your name]
Still curious? Talk to us.
No. Module 1 starts from zero and takes you step by step into real code. Give it 60–90 minutes a day.
In sequence. Complete a module and the next one opens, so every new skill builds on the last. You can read the full curriculum of every module before you enrol.
Nobody can promise you a job, and anyone who does is selling you something. What you will have: the skills in today's AI-engineer job descriptions, 12 projects to show and a verified readiness score.
We turn three this year. To celebrate three years of teaching AI, the complete program is at its anniversary price. After the offer ends, the price returns to ₹56,999.
A laptop and an internet connection. Labs run right in your browser, so there's nothing heavy to install.
Self-paced. Start any day, study at your own hours and revisit any lesson whenever you need to.
Ask within 7 days, with under 20% of the course completed, and you get a full refund. No questions asked.
Your name, what you completed, the date and a public verification ID any employer can check online.
At least 3 years, including every update. Details are in our Service Delivery and Access Policy.
Instalment options, when available, are shown in the pricing box and at checkout. No hidden fees.
AI vs ML vs DL vs GenAI: deep notes, a hands-on lab and a quiz. No card. No catch.