AI and Machine Learning · Raaha
Intensives
AI and Machine Learning From clean data to a deployed, documented model.
Build the data and modelling skills applied ML roles need. You'll clean real Indian public data, train and evaluate models honestly, audit them for bias, and deploy a documented model API.
Skills
18
15 required for the role
Total work
59.7h
at the reference pace
What you're aiming at
The role A junior ML engineer or data scientist prepares data, builds and evaluates models without leakage, communicates results and limits, and helps put models into use responsibly.
ML Intern Junior Data Scientist Data Analyst (ML track) Associate ML Engineer
A credible portfolio in this field has
✓ Two GitHub repositories with clear READMEs✓ A deployed model with a model card✓ Tracked experiments and an evaluation you can defend✓ An honest note on AI useSpecialisations
What you can focus on in phase 3 Tabular ML & analytics Natural language processing Computer vision
18 skills
The skill map Each one is verified by evidence, never by marking a lesson complete.
PHASE 1 · FOUNDATION
Python foundations NumPy pandas Data cleaning Data visualisation Probability and statistics
PHASE 2 · APPLICATION
Feature engineering Supervised learning Model evaluation Data leakage
The plan
What each phase asks of you PHASE 1 · WK 1–3
Foundation
Repair gaps, learn core concepts and tools, and produce small evidence.
14 tasks · 13.1h
Exercise Python warm-up drills wk 1 · 1h Lesson Python for data work wk 1 · 50m Exercise NumPy on real numbers wk 1 · 1.5h Exercise Stretch: broadcasting puzzles wk 1 · 40m Proof of ability
Projects What you'll have built and can show someone.
Crop yield analysis for Odisha districts
Explore and model district-level agricultural data.
about 10 hours
Portfolio-grade Citizen complaint router
Classify public grievances by department to speed up responses.
about 15 hours
Portfolio-grade Deployed prediction API with a model card
Ship a model as an API with validation, monitoring notes and documentation.
about 20 hours
Unsupervised learning NLP basics Computer vision basics Bias and responsible AI Experiment tracking
PHASE 4 · PROOF OF ABILITY
Model deployment API integration GitHub and documentation
Violet marks skills an employer expects at entry level. States run Exposed → Practising → Demonstrated → Verified.
Reflection
Week 1 reflection
wk 1 · 20m
Lesson pandas essentials wk 2 · 1h
Exercise Clean a messy dataset wk 2 · 2h
Exercise Stretch: groupby challenge wk 2 · 45m
Reflection Week 2 reflection wk 2 · 20m
Exercise Statistics refresher wk 3 · 1h
Lesson Probability and statistics for ML wk 3 · 1h
Exercise Exploratory data analysis wk 3 · 2h
Quiz Data foundations check wk 3 · 20m
Reflection Week 3 reflection wk 3 · 20m PHASE 2 · WK 4–6
Application
Mini-projects, practical exercises, peer review and applied assessments.
14 tasks · 13.1h
Lesson Supervised learning wk 4 · 1h Exercise Your first classifier wk 4 · 2h Peer review Review a squad member's work wk 4 · 30m Reflection Week 4 reflection wk 4 · 20m Exercise Feature engineering wk 5 · 1.7h Lesson Choosing evaluation metrics wk 5 · 50m Exercise Cross-validation and tuning wk 5 · 1.5h Peer review Review a squad member's work wk 5 · 30m Reflection Week 5 reflection wk 5 · 20m Lesson Data leakage and common pitfalls wk 6 · 45m Mini-project Mini-project 1: analysis and model wk 6 · 2.5h Quiz Modelling and evaluation check wk 6 · 20m Peer review Review a squad member's work wk 6 · 30m Reflection Week 6 reflection wk 6 · 20m PHASE 3 · WK 7–9
Specialisation
Pick a focus area, work on real cases and build a larger project.
16 tasks · 17.2h
Lesson Unsupervised learning wk 7 · 50m Exercise Customer segmentation wk 7 · 1.3h Exercise Specialisation: gradient boosting wk 7 · 1.8h Exercise Specialisation: text classification wk 7 · 1.8h Exercise Specialisation: image classification wk 7 · 1.8h Exercise Project 2 brief wk 7 · 45m Reflection Week 7 reflection wk 7 · 20m Lesson Bias and responsible AI wk 8 · 50m Exercise Bias audit of your model wk 8 · 1.5h Exercise Track your experiments wk 8 · 1.5h Peer review Review a squad member's work wk 8 · 30m Reflection Week 8 reflection wk 8 · 20m Milestone Mini-project 2: tracked model wk 9 · 2.5h Exercise Stretch: error analysis wk 9 · 45m Peer review Review a squad member's work wk 9 · 30m Reflection Week 9 reflection wk 9 · 20m PHASE 4 · WK 10–11
Proof of Ability
A portfolio-grade project, documentation, review and revision.
10 tasks · 9.9h
Lesson Serving models with an API wk 10 · 50m Milestone Final project: deployed model wk 10 · 2.5h Portfolio README and model card draft wk 10 · 1.3h Peer review Review a squad member's work wk 10 · 30m Reflection Week 10 reflection wk 10 · 20m Revision Revise your final project wk 11 · 2h Defence Project defence wk 11 · 40m Portfolio Publish your README and model card wk 11 · 1h Peer review Review a squad member's work wk 11 · 30m Reflection Week 11 reflection wk 11 · 20m PHASE 5 · WK 12–13
Opportunity Readiness
Resume, portfolio, project defence and your next 30 days.
8 tasks · 6.4h
Career Resume for ML and data roles wk 12 · 1h Exercise Explain-a-model interview practice wk 12 · 1.3h Portfolio Finalise your portfolio profile wk 12 · 1h Reflection Week 12 reflection wk 12 · 20m Assessment Final evaluation: model review wk 13 · 1h Portfolio Final demo video wk 13 · 1h Career Your next 30 days wk 13 · 30m Reflection Week 13 reflection wk 13 · 20m