Overview
A structured, project-first bootcamp covering supervised and unsupervised learning, model evaluation, feature engineering, and deployment patterns used by real teams. You will work in a fixed cohort with weekly milestones, code reviews, and mentor office hours.
Who it's for
- Software engineers moving into ML roles
- Analysts and scientists who want end-to-end ML engineering skills
- Anyone with Python basics ready for a rigorous weekly commitment
What you will learn
Curriculum breakdown, from foundations to portfolio-ready work.
Foundations & tooling
Python for ML, environments, reproducibility, and data pipelines.
- NumPy, pandas, and vectorized workflows
- Experiment tracking and versioning basics
- Train/validation/test discipline
Core algorithms
Classical ML with clear intuition and implementation-level understanding.
- Linear models, trees, ensembles, and boosting
- Clustering and dimensionality reduction
- Biasāvariance and cross-validation deep dive
Applied ML & deployment
From notebook to a maintainable service.
- Model packaging and batch vs online inference
- Monitoring drift and basic MLOps hygiene
- Capstone: end-to-end pipeline on a real dataset
Key features
- Live cohort sessions + recorded core lectures
- Hands-on labs and weekly assignments
- 1:1 office hours and group code reviews
- Mock technical discussions aligned to ML interviews
- Career-oriented portfolio project
Projects
Structured prediction pipeline
End-to-end tabular ML with evaluation reports and reproducible training scripts.
Capstone ML system
Train, evaluate, and document a model with a simple deployment story and monitoring checklist.
Outcomes
Skills gained
Solid fundamentals in training, evaluation, and shipping ML responsibly.
Job readiness
Ability to discuss trade-offs, metrics, and failure modes in interview settings.
Portfolio
Two polished projects with READMEs suitable for hiring managers.
Your mentor
Dr. Ananya Mehta
Lead ML Engineer Ā· ex-research lab
12+ years in applied ML and mentoring
Ananya has shipped ranking and forecasting systems at scale and has guided hundreds of engineers into ML-heavy roles.
Duration & schedule
10 weeks
12ā15 hours (live + async)
Kickoff weekend + weekly live workshops; async lectures and deadlines mid-week.
Ready to join the next cohort?
Secure your seat or request the full syllabus. We'll confirm prerequisites and start dates.
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