The binge
ondemand_video
Watch
Hours of tutorials. Notes you never open. Progress that is really just playtime.
NeuraApp
Learn AI by building it.
Structured learning paths, hands-on coding labs, real projects, and an AI mentor to help you become job-ready.
Reps for the job. Not another binge.
Free to start · Learn in your browser · Build real projects
Built for people who want to ship, not just watch.
Hands-on learningInteractive labsReal projectsAI mentorshipCareer focused
The shift
Watching is not the same as training.
Most people collect tutorials. NeuraApp is built for the other loop — write, fail, ask, and ship.
The reps
terminal
Practice
Write the function. Fail the tests. Debug. Repeat until it sticks.
The proof
handyman
Build
Turn the lesson into a project you can iterate on and show.
Features
One loop. Everything you need to keep moving.
Learn, practice, ask Nea, and build in the same product — not five disconnected tabs.
Learning path
A spine from Python and math through ML, LLMs, and MLOps. Always know the next lesson — and run it in the lab.
Explore pathLearn
Practice
Ask
Build
Prove
Interactive labs
Write, run, and test in the browser. The lab is part of the lesson — not homework you do later.
- In-browser editor
- Hidden test cases
- Instant run
Ask Nea
Not a generic chatbot. Nea already knows the module you are on.
- Hint on overfittingNow
- Next: feature engineeringQueued
- 8 / 10 tests passingLab
How it works
Learn → Practice → Ask → Build → Prove
Five stages, one workspace. Character art from the product — the same loop you follow after you sign in.
01
Learn
Structured lessons built around practical engineering skills — not a pile of disconnected videos.
Paths · lessons · quizzes
02
Practice
Write the code yourself in browser labs. Run tests, fail, debug, and keep the muscle memory.
In-browser IDE · DSA
03
Ask Nea
A lesson-aware mentor that knows where you are in the path and helps you move forward — hints first, not dumped answers.
Context-aware help
04
Build
Turn what you learned into playground projects you can iterate on and show.
Projects · playground
05
Prove
Progress, certificates, and career tools so your work is evidence — not just watch time.
Portfolio · certificates
Paths
Pick a track. Stay on the spine.
Python through MLOps — the same routes you will find inside the product.
codeFoundation
Python
Build the foundation for AI and data work.
Beginner
account_treePractice
Data Structures & Algorithms
Think like an engineer with pattern-based practice in the IDE.
All levels
psychologyCore
Machine Learning
Train models and understand the fundamentals.
Beginner → Intermediate
neurologyCore
Deep Learning
Build neural networks from the ground up.
Intermediate
auto_awesomeApplied AI
LLMs & Prompting
Work with modern models, RAG, and generation systems.
Intermediate
cloud_uploadSystems
MLOps
Move models from notebooks toward production systems.
Intermediate
Inside the product
The lab writes with you. Nea already read the lesson.
Editor, tests, and a mentor that knows the module you are on — not a chatbot you have to brief from scratch.
two_sum.py · lab
Rundef two_sum(nums, target):
seen = {}
for i, n in enumerate(nums):
need = target - n
if need in seen: return [seen[need], i]
Tests
- ✓ [2,7,11] → 9
- ✓ [3,2,4] → 6
- ✗ empty input
WriteRunFailDebugPass
Write → Run → Fail → Debug → Pass. The loop is the curriculum.
Ask Nea
On this lesson · Model evaluation
Why is this model overfitting?
N
Training accuracy keeps rising while validation starts dropping. Inspect regularization and dataset size before adding more capacity.
Hints first. Answers when you have earned them.
Projects
Ship something you can show.
Playground directions tied to real paths — RAG, ML, LLMs, and the road to production.
RAG assistant
Embeddings, retrieval, and generation in one playground project.
RAGVector DB
ML prediction system
Train, evaluate, and iterate on a classical or deep model.
MLEval
LLM application
Prompting, tools, and structured outputs around a real model API.
LLMPrompts
Deployment path
Take a notebook workflow toward monitoring and production habits.
MLOpsServing
Model
LLMs · RAG · fine-tuning
Framework
PyTorch · Hugging Face · LangChain
Kernel
CUDA · attention · autograd
Systems
MLOps · serving · vector DBs
Who it's for
Students, engineers, builders.
Same product. Different entry points. No invented job-placement claims — just a loop that produces work.
school
Students
Build fundamentals, practice in the IDE, and turn coursework into playground projects you can show.
Path → practice → portfolio
sync_alt
Software engineers
Transition into AI engineering with structured ML, LLM, and systems paths — without abandoning your existing engineering instincts.
Software → AI engineering
precision_manufacturing
AI builders
Go deeper than the API: CUDA, RAG, vector databases, and MLOps sit on the same learning spine.
Build and operate AI systems
“This is where developers become AI engineers — by writing the code, not collecting another tutorial.”
12+
Learning tracks
180+
Modules & lessons
50+
Practice problems
Pricing
Start free. Upgrade when the reps get serious.
No credit card to begin. Compare the full matrix on the pricing page.
See full pricing →Free
$0
Learn and build across the entire engineering catalog
- checkFull access to all courses & roadmaps (AI, Full-Stack, Robotics, MLOps)
- checkIn-browser coding labs & DSA practice (zero setup)
- checkInteractive hardware playground for Arduino & IoT
- checkAsk Nea tutor (10 questions a day when stuck)
- checkCommunity discussions & student jobs feed
- checkTrack your progress with daily streaks and badges
Pro
$5/mo
Your personal mentor and career accelerator
- checkAll Free features included
- checkUnlimited Ask Nea tutor whenever you get stuck
- checkATS resume scanner with missing keywords & bullet rewrites
- checkMock interviews with personalized feedback
- checkReal interview coding questions from top companies
- checkVerified completion certificates to share on LinkedIn
- checkDownloadable cheat sheets to keep handy
- checkGitHub profile & project reviews before you apply
- checkPriority support whenever you have questions
Team
$15/mo
For study groups, college clubs & small classrooms
- checkFull Pro access for everyone in your group
- checkOne shared dashboard to learn and build together
- checkCohort progress tracking so no one falls behind
- checkSingle consolidated bill for the entire team
- checkDirect help getting your cohort started
Questions, answered
Yes. You can start on the Free plan with no credit card. Pro and Team unlock more Ask Nea usage, playground capacity, and team controls. Compare plans on the pricing page.
Students, software engineers, and builders who want to learn AI by practicing and shipping — not by binge-watching tutorials.
No. Start with Python or Math Foundation, then move into machine learning, deep learning, LLMs, and MLOps at your own pace.
Yes. The in-browser lab includes an editor, tests, and run/submit workflows for DSA and coding practice. The ML playground lets you build projects without a local setup.
Ask Nea is NeuraApp’s lesson-aware AI mentor. It uses the module you are on to give hints, explanations, and next-step guidance instead of acting like a generic chatbot.
Yes. You can create and iterate on playground projects from your dashboard, tied to what you are learning on the path.
AI engineering is the destination. The spine also includes Python, mathematics, DSA, and software engineering so the fundamentals stay connected to ML and production AI.
Yes. Paths, labs, and Ask Nea are self-paced. Progress is saved to your account.
Yes. Completed tracks and assessments can earn verified certificates with shareable verification codes.
If you are new, begin with Python. If you already write software, jump into DSA practice or Machine Learning. The path always shows the next lesson.
Yes. Tracks include PyTorch, RAG, CUDA, and MLOps so you can move from using models to building and operating them.