AG
Data Scientist track — LogicMojo Data Science Candidate working on assignments.
AIMLMachine Learning
LogicMojo AI & ML Learner
Curated by LogicMojo AI mentors — updated each cohort
Last updated
LogicMojo AI Community
Real learners. Real GitHub commits. Real AI, ML, GenAI and Agentic AI projects — reviewed by working engineers.
I'm Monesh, one of the mentors. I built this page from what I've seen across three cohorts: every student here has shipped public assignments, every link below is a live profile, and every project has been peer- and mentor-reviewed. No stock photos, no fake testimonials — just verifiable work.
+243 learners100% verified profiles
Community at a glance
Real numbers, verifiable work
Every metric below is backed by public profiles you can click through and verify yourself.
Featured Learners
249+
Public student profiles
GitHub Profiles
247
Public assignment repos
LinkedIn Profiles
144
Verified learner profiles
AI/ML Assignments
1000+
Weekly mentor-reviewed
Student Projects
150+
Portfolio-ready builds
Active Community
LogicMojo AI
Mentor-guided learners
Meet the community
Meet Our AI Learners
Search and filter through 249+ public student profiles — each one shipping AI assignments and projects on GitHub.
Filter learners
Showing 16 of 249 learners
All profiles verified
Focused on Fine-tuning GPT models.
AIMLGenAIMachine Learning
LogicMojo AI & ML Learner
ML Engineer track — LogicMojo Data Science Candidate building hands-on projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
ML Engineer track — LogicMojo Data Science Candidate building hands-on projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AB
AI Engineer track — LogicMojo Data Science Candidate working on assignments.
AIMLMachine Learning
LogicMojo AI & ML Learner
A
Aspiring Data Engineer — LogicMojo Data Science Candidate building course projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
Software Engineer integrating LLMs into web apps.
AIMLGenAI
LogicMojo AI & ML Learner
Data Analyst track — LogicMojo Data Science Candidate building projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AI Engineer track — LogicMojo Data Science Candidate building projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AI Engineer track — LogicMojo Data Science Candidate working on projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AI Engineer track — LogicMojo Data Science Candidate building assignments.
AIMLMachine Learning
LogicMojo AI & ML Learner
Data Scientist track — LogicMojo Data Science Candidate building hands-on projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AI Engineer track — LogicMojo Data Science Candidate building hands-on projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
Aspiring AI Engineer — LogicMojo Data Science Candidate building portfolio projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AM
Data Analyst track — LogicMojo Data Science Candidate working on course projects.
AIMLMachine Learning
LogicMojo AI & ML Learner
AI Engineer track — LogicMojo Data Science Candidate building assignments.
AIMLMachine Learning
LogicMojo AI & ML Learner
Featured AI Learners
Active GitHub Contributors
A handpicked group of learners actively shipping projects, sharing assignments, and building public AI portfolios.
Featured
Senior AI Engineer building scalable LLM applications.
AIMLGenAI
LogicMojo AI & ML Learner
Featured
AI Scientist specializing in Generative Models.
AIMLGenAIMachine Learning
LogicMojo AI & ML Learner
Featured
ML Engineer focused on RAG and Vector Databases.
AIMLRAGMachine Learning
LogicMojo AI & ML Learner
Featured
AI enthusiast finetuning LLaMA and Mistral models.
AIMLGenAIMachine Learning
LogicMojo AI & ML Learner
Featured
Deep Learning student building Vision Transformers.
AIMLGenAIComputer Vision
LogicMojo AI & ML Learner
Featured
AI Engineer implementing Multi-Agent Systems.
AIMLMachine Learning
LogicMojo AI & ML Learner
Featured
GenAI practitioner working on Prompt Engineering.
AIMLGenAI
LogicMojo AI & ML Learner
Featured
Data Science practitioner exploring ML applications.
AIMLMachine Learning
LogicMojo AI & ML Learner
What we expect on your GitHub
The kind of GitHub work we ask for
Below are the seven repository types I expect every serious learner to ship by the end of the program. I've seen these exact categories open doors for our alumni at product companies and AI startups.
01
AI & ML Assignments
Weekly graded work — I personally review a sample from each batch to keep the bar high.
02
Python Practice
Notebooks I've curated from problems I've actually hit in production ML work.
03
Machine Learning Projects
Regression, classification and full pipelines — the kind I'd ask about in an interview.
04
Deep Learning Projects
CNNs, RNNs, Vision Transformers — built on datasets where the modeling choices actually matter.
05
Generative AI Projects
LLM apps, fine-tuning runs, and prompt engineering with proper evaluation, not vibes.
06
RAG & Agentic AI
Vector DBs, retrieval, and multi-step agents — the patterns I'm shipping at work today.
07
Portfolio Repositories
Polished repos with README, screenshots and run instructions. This is what recruiters open.
Why this matters
Why I built this community page
These are the seven things I tell every new cohort on day one — written from what I've actually watched work, not generic advice.
Insight 01
Public GitHub portfolios that recruiters can verify
In my last cohort, learners who pushed assignments weekly got 3–4× more recruiter outreach on LinkedIn than those who didn't. Public commits are the cheapest credibility you can buy.
Insight 02
A LinkedIn presence built from real work
I coach students to share each finished project as a LinkedIn post with the GitHub link. Several have landed interviews directly from those posts — not from cold applications.
Insight 03
Weekly assignments build the muscle
After reviewing 1000+ submissions, the pattern is clear: consistency beats intensity. A learner shipping one small thing every week outperforms someone cramming for a month.
Insight 04
Practical AI skills, not just course videos
Our projects mirror what I actually build at work — RAG pipelines, fine-tuning runs, evaluation harnesses. You can read the repos and see the engineering, not just the model call.
Insight 05
Hiring managers can read your code
I've sat on hiring panels for AI roles. A clean GitHub with end-to-end projects moves a candidate past the resume screen faster than any certificate I've seen.
Insight 06
You learn faster with peers shipping next to you
Most of my best students didn't learn from me alone — they learned from reading each other's pull requests and copying patterns that worked.
Insight 07
Mentor reviews from people who do this for a living
Every assignment is reviewed by working AI engineers — myself included. We comment on data leakage, prompt design, eval metrics, deployment — the things textbooks skip.
The 22-week roadmap
The journey I've walked with every cohort
This isn't a marketing roadmap. It's the actual 22-week path I've watched 500+ learners take — with the friction points and wins I've seen along the way.
- 1
Week 0 — Enroll and pick your track
I help every new learner choose between AI Engineer, Data Scientist, ML Engineer, Data Analyst or Data Engineer based on their background. Wrong track is the most expensive mistake I've seen people make.
- 2
Weeks 1–6 — Fundamentals that actually transfer
Python, SQL, Statistics, and core Machine Learning. I keep this tight on purpose — too much theory upfront kills momentum. We ship a small notebook every week.
- 3
Weeks 7–14 — Weekly graded GitHub assignments
Real PR reviews from me and the mentor team. We catch leakage, sloppy splits, and untested code before they become habits.
- 4
Weeks 15–22 — End-to-end projects
ML, Deep Learning, RAG, Agentic AI. You pick a problem you actually care about — that's the only way the project gets finished.
- 5
Throughout — Public GitHub portfolio
Each project lives in its own repo with a real README, screenshots, and how-to-run instructions. This is what recruiters click on.
- 6
Throughout — Learn-in-public on LinkedIn
I personally review the LinkedIn posts of learners who opt in. A weak post with a strong project still beats silence.
- 7
Final weeks — Mock interviews with working engineers
ML system design, coding rounds, and project deep-dives. I run a lot of these myself — the feedback is direct and unfiltered.
Mentor notes from the trenches
What I've actually seen work in this community
Four mentor-tested patterns I've watched separate the hired learners from the rest.
Insight 01
GitHub as a public log of work
After mentoring three full cohorts inside the LogicMojo AI Community, the single biggest predictor of a learner getting hired isn't their starting background — it's whether they treat their GitHub as a public log of work. The students featured on this page do exactly that, week after week.
Insight 02
Clean repos that read like an engineer wrote them
When I look at strong AI student projects here — a clean RAG pipeline from Sourav, a Vision Transformer build from Manikandan, an MLOps deploy from Nitin — what stands out isn't the model choice. It's that each repo reads like an engineer wrote it: clear README, sensible folder structure, working install instructions, an honest results section. That's the machine learning student portfolio recruiters actually open.
Insight 03
Weekly cadence beats monthly cramming
I've also watched the opposite. Learners who stopped pushing for a month saw their momentum die. The ones who shipped small AI learner GitHub projects every single week — even messy ones — kept improving fast. That's why our cadence is weekly, not monthly.
Insight 04
Verify any AI community before you join
If you're evaluating an AI & ML course community, my honest advice: don't take any provider's word for it. Click through the GitHub links on this page. Read the repos. Look at the commit history. That's the only student GitHub assignment showcase you can actually verify — and it's why we publish it openly.
“The only honest AI community is the one whose GitHub links you can click on, today, and verify yourself. Everything else is marketing.”
— Monesh Venkul Vommi, Lead AI Mentor at LogicMojo. Last reviewed for this cohort: this month.
Frequently asked
Frequently Asked Questions
Real questions from learners I've worked with — answered directly, no fluff.
Still have a question? Use the Join Course button at the top — a career advisor will reach out the same day, and I personally help with track selection.
Ready when you are
Ready to be the next learner on this page?
If you're willing to ship publicly every week, I'll personally help you go from your first commit to a portfolio strong enough to be featured here. That's the promise of the program — mentor-led, GitHub-first, no fluff.
500+
learners mentored
150+
portfolio projects
22 weeks
end-to-end roadmap