Advanced Data Science
Machine Learning & MLOps
Go beyond theoretical algorithms into production machine learning. Learn data preprocessing, feature engineering, neural network architectures, and the MLOps pipeline to monitor model drift and performance.
Curated 2026 Curriculum GuideProject-Based Track
PythonScikit-LearnPyTorchTensorFlowMLflowFastAPIDocker
🇮🇳 Indian Market Benchmark
Expected CTC Range₹9.0L – ₹26.0L LPA
Estimated Timeline16 – 22 Weeks
Demand Scope7,500+ Openings
Experience LevelAdvanced
Top Hubs:Bengaluru, Hyderabad, Gurugram, Pune, Noida
Explore Career Compass MatchCore Track Highlights
Premium package ceilings in Indian FinTech, E-Commerce recommendations, and Healthcare
Transition path for senior data analysts seeking deep algorithmic modeling
Focus on real-time serving, low latency inference, and continuous retraining
Structured Phase-by-Phase Syllabus
Focus on build-by-doing milestones rather than passive video consumption.
Weeks 1 - 6
Phase 1: Math Foundations & Supervised Algorithms
- Linear Algebra, Probability, Calculus intuitions
- Regression, Decision Trees, Random Forests, XGBoost
- Hyperparameter tuning with Optuna and cross-validation
🎯 Milestone Proof Project: Indian Used Car Price Valuation Predictor with feature importance analysis.
Weeks 7 - 14
Phase 2: Deep Learning & Computer Vision / NLP
- Neural networks, backpropagation, and PyTorch tensors
- CNNs for image classification and Object Detection (YOLO)
- Transformers, BERT, and sentiment analysis
🎯 Milestone Proof Project: Medical Chest X-Ray Disease Classification Model with 92%+ accuracy.
Weeks 15 - 22
Phase 3: MLOps, Model Deployment & Monitoring
- Model packaging with ONNX and FastAPI serving
- MLflow experiment tracking and model registry
- Detecting data drift and concept drift in production
🎯 Milestone Proof Project: Real-time FinTech Credit Card Fraud Detection Microservice with latency SLA under 50ms.
Technical Interview Questions & Answers
Q1: How do you handle severe class imbalance in a dataset (e.g. 99% non-fraud, 1% fraud)?
Use techniques such as SMOTE (Synthetic Minority Over-sampling), cost-sensitive loss functions (focal loss), undersampling majority classes, and evaluate using Precision-Recall AUC (PR-AUC) or F1-score rather than accuracy.
Frequently Asked Questions
Is a Master’s degree compulsory for Machine Learning?
Not strictly, but strong mathematical maturity and documented Kaggle or GitHub implementations are heavily screened.
Target Job Roles
Junior Data Scientist
Demand: HighMachine Learning Engineer
Demand: HighLead AI Scientist
Demand: ModerateRelated Career Tracks
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