ML is not magic. It's math + Python + patience — and one of the highest-paying paths for Indian engineers who commit to it.
Start your roadmapPeople imagine: training neural networks all day, building the next ChatGPT, watching models learn. Reality: 60% of the job is data cleaning and feature engineering. The model training part — the thing everyone thinks ML is — is maybe 10% of the work.
A real ML engineer's week: 3 days cleaning and transforming messy data into something a model can use, 1 day building and tuning models, half a day evaluating if the model actually works on new data, and the rest debugging why the FastAPI endpoint is throwing a 500 error in staging.
The irony of the job: the 'AI that will replace humans' needs humans to babysit it 90% of the time. That's job security.
In priority order. Learn these in sequence, not all at once.
Start from 01 and work forward. Each topic unlocks the next. Don't jump to topic 21 before you own topic 9.
We are not another course provider.
Every topic below is backed by the best free YouTube videos for that exact concept — handpicked, not random. Everything you need to become an ML engineer is already on YouTube. You just need someone to find the right video, put it in the right order, and tell you what to actually do with it. That's what this roadmap does. No ₹10,000 course required.
Ready to start? The roadmap is live.
Click any topic, read the concept, see the resources. First 7 topics are completely free.
CTC vs in-hand: always ask for the breakup before celebrating an offer.
ML roles pay better than most software roles — but the entry bar is also higher. A '20 LPA' ML offer at a startup is often 60% base + 40% variable + stock that may or may not vest. Always ask for the breakup.
Data analyst work, basic ML tasks, Excel + Python. Common fresher entry point.
Swiggy, Meesho, Razorpay, healthtech startups. Real ML problems, real pay jump.
Flipkart, Google India, Microsoft, Amazon. Needs solid portfolio + DS/Algo skills.
Google Brain, DeepMind India, OpenAI. Extremely competitive, usually needs research background.
One clear path. Follow it in order. 2–3 hrs/day after college or work.
Not a tutorial sprint — build a data cleaning project from a messy Kaggle dataset. Merge, groupby, fillna, and plot should feel automatic.
Linear algebra, partial derivatives, probability distributions. Use 3Blue1Brown's Essence of Linear Algebra + StatQuest on YouTube. Don't memorise — understand.
Train and evaluate every major model: regression, trees, forests, SVM, K-Means. Use the TomoLink ML roadmap — it covers all 4 phases in sequence.
Join any beginner competition. The goal isn't to win — it's to read other people's notebooks, try things, and write a public notebook others can see. This is your first portfolio piece.
Build a model, expose it via FastAPI, and host it. Even a house price predictor wrapped in an API beats 10 notebooks nobody can use. This is the portfolio piece that gets you interviews.
ML roles at product companies still test DSA. Not LeetCode Hard — but arrays, strings, and dynamic programming basics come up. Do NeetCode 150 in parallel.
GitHub with real projects + a Kaggle profile with at least 1 public notebook + LinkedIn with your project descriptions. Referrals from ML communities (Discord, LinkedIn groups) beat cold applications 10x.
ML interviews are scarier than SDE interviews because they test three things at once: coding (DSA), ML concepts (theory + math intuition), and your actual project experience. Most people freeze not because they don't know — but because they haven't explained their projects out loud enough times. Fix that with mock interviews, not more studying.
Know bias-variance tradeoff cold — explain it without jargon, with a real example from your project
Be ready for 'why did you choose Random Forest over Logistic Regression' — the 'why' is what they're testing
Explain F1 vs accuracy with a real imbalanced dataset example (fraud detection, disease prediction)
Know what overfitting looks like and 3 ways to fix it (regularization, cross-validation, more data)
Walk through your end-to-end project confidently: data source → cleaning → model choice → evaluation → deployment
For FAANG-level: prepare for probability questions, A/B testing statistics, and system design for ML pipelines
For ML roles, cold applications barely work — most roles are filled through referrals or from Kaggle/GitHub visibility. Post your projects on LinkedIn with actual results ('model achieved 94% accuracy on X dataset'). ML hiring managers scroll LinkedIn for this. One viral project post can get you 10 DMs.
Stop collecting tabs. The roadmap is here, the questions are curated, and the first 3 topics are free. One step at a time.