Lab 01
Credit Risk Scoring Model
Train a gradient-boosted model on tabular loan data, calibrate thresholds and explain predictions with SHAP.
Become an AI / ML engineer in 16 weeks.
Applied ML with real datasets, from EDA to model deployment. Ship 5+ portfolio models mentored by working data scientists — cover supervised, unsupervised, feature engineering and MLOps basics.
0% EMI availablePlacement guaranteeHands-on labs included
AI & Machine Learning Track
Only 6 seats left
30-min call · no pressure · get a personal roadmap
16 wks
Cohort length
55+
Labs & notebooks
9.3/10
Alumni rating
91%
Placement rate
Alumni working at
DNMPramataResponsiveSocial Bytes Pvt LtdUPLDMG
Outcomes
What you'll be able to do on day one at your new job
Every module ends with a graded lab that mirrors what teams actually ship. No slides-only theory.
Frame a business problem as a supervised or unsupervised ML task.
Run end-to-end EDA, cleaning and feature engineering pipelines.
Train and tune Linear, Tree, XGBoost and clustering models.
Evaluate models with AUC, F1, RMSE and cross-validation properly.
Explain models using SHAP and feature importance.
Track experiments and register models using MLflow.
Serve models via FastAPI + Docker on a cloud VM.
Diagnose data drift and set up basic retraining workflows.
16-week curriculum
Get the detailed syllabus PDFSix modules. From regression to production.
- Module 01Weeks 1–3
Python + Math for ML
The working foundations every ML engineer needs.
- Python, NumPy, Pandas
- Linear algebra & calculus refresher
- Probability & statistics
- Matplotlib & Seaborn for EDA
- Module 02Weeks 4–6
Supervised Learning
Regression and classification with rigor.
- Linear & Logistic Regression
- Decision Trees & Random Forest
- Gradient Boosting: XGBoost, LightGBM
- Model evaluation & cross-validation
- Module 03Weeks 7–8
Unsupervised Learning
Structure without labels.
- K-Means, DBSCAN, Hierarchical
- PCA & t-SNE
- Anomaly detection
- Recommender systems basics
- Module 04Weeks 9–11
Feature Engineering & Model Tuning
Where 80% of accuracy actually comes from.
- Encoding, scaling, imputation
- Feature selection strategies
- Hyperparameter tuning: GridSearch, Optuna
- Handling imbalanced datasets
- Module 05Weeks 12–14
MLOps Foundations
Ship models, not notebooks.
- MLflow experiment tracking
- Model registry & versioning
- FastAPI + Docker deployment
- CI/CD for ML pipelines
- Module 06Weeks 15–16
Capstone & Interview Prep
Portfolio and hiring readiness.
- End-to-end capstone project
- Mock interviews
- Case study frameworks
- Resume + LinkedIn review
Mandatory capstone labs
Projects you'll actually ship
Lab 02
Customer Segmentation Dashboard
Cluster e-commerce customers, visualize personas with PCA and ship an interactive dashboard for the marketing team.
Lab 03
Production ML API
Serve a trained model behind a FastAPI endpoint, containerize with Docker, and deploy to a cloud VM with monitoring.
1,200+
ML alumni placed
150+
Hiring partners
8 yrs
Training experience
12
Max cohort size
Student stories
Real placements. Real hikes. Real portfolios.
"I finally understood why cross-validation matters — my interview answers went from generic to sharp overnight."
N
Nikhil Rao
ML Engineer · fintech
"The MLflow + FastAPI module gave me the one thing every junior ML resume is missing: deployment."
S
Sneha Iyer
Data Scientist
"My capstone became my portfolio piece. The interviewer literally asked to walk through my GitHub."
R
Rahul Menon
Analyst → ML Engineer
"Mentors reviewed my code weekly. That feedback loop was worth the fee alone."
A
Ananya Das
Junior Data Scientist
FAQ
Everything parents & partners ask
Do I need coding experience?
Basic Python helps. If you're new, we include a 2-week Python primer at the start of the cohort.
How is this different from Deep Learning?
This track focuses on classical ML — the models 80% of industry actually uses in production. Deep Learning is a separate track for CV/NLP roles.
What roles do alumni get placed into?
ML Engineer, Data Scientist, Analytics Engineer and Applied Scientist roles, typically ₹8-24 LPA depending on experience.
Is the course online or in-person?
Hybrid. Live weekday classes online, weekend labs and mock interviews at our Chandra Layout campus in Bengaluru (or fully online).
What tools will I use?
Python, Pandas, scikit-learn, XGBoost, MLflow, FastAPI, Docker and a cloud VM for deployment. All tools are free or covered by the course sandbox.
Is there placement support?
Yes — resume rewrites, 3 mock interviews, portfolio reviews and direct referrals to 150+ hiring partners. Support continues until you're placed.
Next cohort · Aug intake
Your Data Engineering career starts with a 30-min call.
Talk to a mentor. Get an honest read on your background, a personalized roadmap, and full fee + EMI details. No pressure.