Paid AI engineering course · INR 4,999
Engineering is simple.
As simple as A, B, C.
Learn AI, ML, cloud, and full-stack engineering through written lessons, real projects, and a deployment-first curriculum led by Divyanshu Singh Chouhan.
- Lessons
- 20
- Modules
- 4
- Notes
- 40
Lesson preview
Zero to deployed AI app
The transformation arc
Four modules. One continuous shape.
Each module produces a working artefact and a verified milestone. The course is designed so each step makes the next one obvious.
- A
- B
- C
- D
The actual stack
Tools you will use, not just hear about.
These are the technologies the course works with directly. No marketing logos. If a tool is on this wall, you will configure it, run it, and ship something with it.
Antigravity
AI IDE
Gemini
Model API
TypeScript
Type Safety
Node.js
Runtime
Docker
Containers
SQLite
Local Storage
Git
Version Control
Cloudflare
Edge & Tunnel
Learning shape
Read, build, review, then ask sharper questions.
Every lesson is written first. Videos can support a written lesson, but the core understanding has to survive on the page. This is what makes the curriculum verifiable and reviewable.
Read
Written lessons are the primary product
Every course page should explain the module clearly enough that a learner understands the value before enquiring.
Build
Each lesson produces an artefact
Code, configuration, terminal output, repository — something verifiable that did not exist before the lesson.
Review
Recall locks understanding
After every lesson there is a short review note the learner uses to write back what was learned.
Scale
Downloads before dashboards
PDFs, workbooks, command sheets, and rubrics serve enrolled learners before a portal or login system is worth building.
From the engineering blog
Free deep-dives, written for engineers.
The blog is the public surface of the curriculum. Each article is a deep model that pairs with a specific lesson — read the article, then apply it hands-on.
React Native vs Flutter: Complete Comparison for 2026
How React Native (JSI + Fabric) and Flutter (Impeller + Dart) actually differ in 2026 — architecture, language, performance, ecosystem, and which to pick for your project.
10 min read
Docker Compose Explained: Multi-Container Applications Made Simple
How docker-compose.yml turns a stack of containers into one declarative file — services, networking, volumes, health checks, and the difference between image and build.
9 min read
GitHub for Beginners: Creating Your First Repository and Pull Request
From `git config --global` to your first merged pull request. Local Git workflow, branching, conflicts, forking, and the protections that keep main branches safe.
10 min read
Course fee
One paid path. Direct enrollment.
The course is paid. Enrollment happens by WhatsApp or call — no payment gateway, no login wall, no fake urgency. After confirmation you receive the static study pack and direct guidance from Divyanshu.
- 20-lesson AI, ML, cloud, and full-stack engineering path
- Module-wise labs, review notes, and project checkpoints
- Downloadable study pack prepared for enrolled learners
- Direct WhatsApp coordination with Divyanshu
Course tier · Self-paced
ABCsteps AI Engineering Course
INR 4,999
20 lessons · 4 modules · downloadable learner pack
No payment gateway · No dashboard yet · WhatsApp +91 96100 01234
See all 5 tiers (Cohort · Mentorship · Architecture Review · Workshops)Five paid paths
Pick what you actually need, not what looks fanciest.
Self-paced course, live cohort, 1:1 mentorship, single-session architecture review, institutional workshops. Every tier uses WhatsApp-first enrollment — no payment gateway in this phase.
Course
INR 4,999
20 written lessons + study pack — at your own pace
Open
Cohort
INR 14,999
per cohort batch
4 weekly group calls + project review
Open
Mentorship
INR 49,999
per mentee · 8-week engagement
4 private 1:1 sessions over 8 weeks
Open
Architecture Review
INR 24,999
per session
One 2-hour deep session + roadmap doc
Open
Workshop
INR 2L–5L
per engagement, scope-dependent
2-day intensive for institutions and teams
Open
Academic outcomes
What a learner should be able to prove after the course.
Outcome 01
Build a working app
Use AI assistance without losing understanding of files, code, and verification.
Outcome 02
Publish and explain work
Use GitHub, documentation, and written notes to show what was built and why.
Outcome 03
Deploy with cloud basics
Understand containers, tunnels, APIs, and deployment checks at a practical level.
Outcome 04
Add AI responsibly
Treat AI as an API-backed product feature with costs, errors, and limits.



