Machine Learning Engineer Master Program

Variables; operators; conditions; loops; functions; strings; lists; tuples; dictionaries; sets.

Classes; objects; constructors; inheritance; encapsulation; polymorphism; abstraction.

Exceptions; modules; packages; file handling; JSON; decorators; generators; comprehensions; typing.

Virtual environments; dependency management; project structure; configuration; environment variables; logging; testing basics.

Scalars; vectors; matrices; dot product; matrix multiplication; transpose; dimensions.

Focus on intuition; Functions; derivatives; gradients; partial derivatives; optimization.

Probability rules; conditional probability; Bayes theorem; distributions.

Mean; median; mode; variance; standard deviation; percentiles; distributions.

Covariance; correlation; causation vs correlation.

Population; sample; sampling bias; representative data.

Null hypothesis; alternative hypothesis; p-value; significance; confidence intervals.

Understand why statistics affects; data sampling; experimentation; feature selection; validation; monitoring.

Creation; indexing; slicing; shapes; dimensions.

Broadcasting; vectorization; numerical operations.

Use NumPy for ML calculations.

Read CSV; Excel; JSON; SQL; filtering; sorting.

Missing data; duplicates; invalid values; data types; outliers.

GroupBy; merge; join; pivot; aggregation.

Discuss; memory usage; efficient types; chunk processing; vectorized operations.

SELECT; WHERE; ORDER BY; GROUP BY; HAVING.

INNER; LEFT; self joins; multi-table queries.

CTE; subqueries; window functions; indexes; query optimization.

Build ML training datasets directly from relational data.

Analyze; Distribution; missing data; outliers; relationships; anomalies.

Use; Histogram; scatter plots; box plots; bar charts; correlations.

Detect; Invalid ranges; duplicated records; schema problems; missing categories; inconsistent values.

Understand training set.

Use validation for; tuning; comparison; selection.

Keep final test data isolated.

Major interview topic; Target leakage; future information; preprocessing before split; duplicate users across train/test.

Critical for; forecasting; fraud; churn; financial applications.

Project; Property Price Prediction.

Project; Customer Conversion Prediction.

Distance; normalization; K selection.

Useful concepts for classification.

Nodes; splits; entropy; information gain; pruning concepts.

Ensembles; bagging; feature sampling; feature importance.

Hyperplanes; margins; kernels.

Understand sequential ensemble learning.

Hands-on; Training; parameters; evaluation; feature importance.

Concepts and practical comparison.

Understand advantages for categorical features.

Project; Customer Segmentation.

Linkage; dendrogram concepts.

Density; noise; outliers.

Dimensionality reduction; variance; principal components.

Strategies; Delete; mean/median; mode; model-based concepts; missing indicators.

One-hot; ordinal; frequency; target encoding concepts.

Standardization; normalization; log transform.

Create; Day; month; hour; weekday; elapsed time; seasonality indicators.

Combine variables meaningfully.

Banking; debt_to_income; Aviation; arrival_delay_minutes; E-commerce; days_since_last_purchase.

Prevent features that reveal target information improperly.

Correlation; statistical tests concepts.

Recursive Feature Elimination concepts.

Feature importance; regularization.

Balance; performance; complexity; explainability; inference cost.

Accuracy; Precision; Recall; F1; confusion matrix; specificity; ROC-AUC; PR-AUC concepts.

MAE; MSE; RMSE; MAPE concepts; R².

Useful for recommendation/search applications.

Instead of automatically using; 0.50; learn threshold optimization.

Fraud model; False negative may cost ₹50,000; False positive may cost ₹20 of investigation; Technical metrics must align with business objectives.

Fraud; failure prediction; disease detection; churn.

Oversampling; undersampling; SMOTE concepts.

Configure algorithms appropriately.

Avoid relying on accuracy alone.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Important when multiple rows belong to the same entity.

Understand model parameters vs hyperparameters.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Introduce modern tuning approaches.

Prevent unnecessary training.

Build; Preprocessing; then; Feature Engineering; then; Model; then; Prediction.

Different transformations for; numeric; categorical.

The same transformations used in training must apply in production.

Instead of filenames; model_final.pkl; model_final2.pkl; model_really_final.pkl; track professionally.

Store; Algorithm; parameters; dataset version; feature version; metrics; artifacts; timestamp.

Hands-on; Experiments; runs; parameters; metrics; artifacts.

Example dashboard; Run: Model: AUC: Recall: Latency; #31: Logistic: .81: .68: 5ms; #32: Random Forest: .87: .76: 20ms; #33: XGBoost: .90: .79: 12ms; Choose models based on multiple constraints.

Lifecycle; EXPERIMENTAL; then; CANDIDATE; then; VALIDATED; then; APPROVED; then; PRODUCTION; then; RETIRED.

Store; model ID; version; algorithm; metrics; dataset; features; author; approvals.

Know exactly; Which data + code + features + parameters produced this model?

Git commit associated with training run.

Datasets must have versions/checksums.

Store; dependency versions; runtime; configuration.

Understand limits and purpose of reproducibility.

Build; POST /predict.

Use Pydantic.

Prediction; probability; model_version.

Handle; missing feature; wrong type; out-of-range values; unavailable model.

OpenAPI / Swagger.

Fraud; recommendations; scoring.

Daily churn score; monthly risk; customer segmentation.

Useful for; computationally expensive predictions; large file processing.

Choose based on; latency; volume; cost; business need.

Images; containers; Dockerfile; ports; environment variables.

Package; Model; preprocessing; FastAPI; dependencies.

Run; ML API; PostgreSQL; monitoring components.

DevOps + Data + ML Lifecycle.

Source control; data versioning; experiment tracking; pipelines; registry; deployment; monitoring; retraining.

Use; DEV; TEST; STAGING; PRODUCTION.

Data; then; Validation; then; Features; then; Train; then; Evaluate; then; Register.

Dataset missing; schema changed; training failed; metric below threshold.

Repeated pipeline execution should not corrupt results.

Commit; then; Lint; then; Unit Tests; then; Data Tests; then; Pipeline Tests; then; Build.

Model cannot proceed if; AUC < approved threshold; or another defined metric fails.

Approved Model; then; Container; then; Staging; then; Validation; then; Production.

If new model fails; v5; then; Rollback; then; v4.

Old; Blue; New; Green; Switch after validation.

5% traffic to New model; 95% to Existing model; Increase gradually.

New model receives traffic but predictions are not used operationally; Useful for comparison.

Compare model business impact.

Track; Model; version; request volume; latency; failures; predictions.

Detect; missing values; unknown categories; invalid ranges; schema changes.

Production feature distribution changes; Training; Average customer age = 34; Production; Average = 51; Investigate.

Relationship between input and target changes.

Prediction distribution changes unexpectedly.

Track changes in which features drive predictions.

When ground truth becomes available; compare production predictions to real outcomes.

A model can have good accuracy while failing business objectives; Track; Technical; precision; recall; latency; Business; fraud prevented; churn retained; revenue; conversion.

Alerts should correspond to meaningful outcomes.

Drift_score > threshold; error_rate > 2%; latency_p95 > target.

INFO; WARNING; CRITICAL.

Data drift detected; then; Investigate; then; Validate upstream data; then; Evaluate model; then; Retrain if required.

Possible triggers; New labeled data; drift; scheduled interval; quality degradation.

Trigger; then; Training Pipeline; then; Evaluation; then; Approval; then; Deployment.

Do not automatically promote all newly trained models; Use approval for; regulated systems; high-risk systems; important business models.

Purpose; reusable features; consistency; lineage; discoverability.

Used during training.

Used for real-time inference.

Prevent different feature calculations between training and production.

Validate; Column; type; required; ranges.

Compare dataset distributions.

Upstream systems should provide expected data structure.

Understand global importance.

Understand why one prediction occurred.

Hands-on interpretation.

Risk Score; 82%; Top Contributors; Debt Ratio: +21%; Late Payments: +18%; Income Stability: -8%.

Understand bias from; Sampling; labels; history; feature selection.

Introduce fairness evaluation.

Protect; personal information; sensitive attributes; training datasets.

Maintain; intended use; limitations; metrics; known risks.

Authentication; authorization; rate limiting; input validation.

Understand potential misuse.

Training data may be manipulated.

Learn basic model robustness considerations.

Protect; model files; training data; credentials.

IAM; EC2; S3; RDS; networking; CloudWatch.

Use S3-style object storage for; datasets; models; reports.

Concept; Storage; then; Training Compute; then; Artifact; then; Registry.

Client; then; API; then; Model Endpoint; then; Prediction.

Exposure to production ML services and managed workflows.

Multiple prediction instances.

Add replicas based on traffic.

Scale based on; requests; CPU; latency.

Avoid repeated expensive loading.

Appropriate only where business semantics allow it.

Measure; preprocessing; model; postprocessing; network.

Predictions per second.

Optimize model/runtime footprint.

A 0.1% accuracy improvement may not justify 10× inference cost.

Components; Compute; storage; data processing; experiments.

Calculate; Requests × Compute × Model Runtime.

Model A; 91.2% accuracy; Cost: ₹X; Model B; 91.5%; Cost: 4× X; Which is better?; Students learn business trade-offs.

User; item; interaction.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Architecture, implementation choices, evaluation trade-offs and production engineering considerations.

Fraud; infrastructure; manufacturing; cybersecurity.