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COURSE

Machine Learning Engineer

Design, train, evaluate and serve practical machine-learning models.

PythonScikit-learnXGBoostML PipelinesFastAPI
Admissions Open
₹29,999 Indicative fee — update before production launch

Duration16 Weeks
FormatOnline / Classroom
LevelIntermediate
ScheduleWeekday / Weekend batches
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Course overview

Go beyond introductory data science into reusable machine-learning pipelines. Learn feature engineering, model selection, evaluation, imbalance handling, tree-based models, experiment discipline and API-based model serving.

What you will be able to do

Build reproducible ML pipelines
Choose suitable evaluation metrics
Train and tune classical ML models
Handle real-world data issues
Serve models through APIs

Curriculum

ML Foundations3 Weeks
Problem types
Train/validation/test
Metrics and baselines
Feature Engineering3 Weeks
Encoding and scaling
Missing values
Leakage and pipelines
Supervised Learning4 Weeks
Linear models
Trees and ensembles
Gradient boosting
Model Evaluation & Tuning3 Weeks
Cross-validation
Hyperparameter tuning
Imbalanced data
Serving ML Models3 Weeks
Model packaging
FastAPI inference API
Monitoring concepts

Projects you can build

Fraud Risk Classifier

End-to-end binary classification pipeline with evaluation and API serving.

Lead Scoring Model

Predictive scoring workflow with explainable business metrics.

Frequently asked questions

Do I need prior experience?

The entry requirement depends on the course level. Beginner courses start from fundamentals; intermediate tracks expect basic programming or domain knowledge.

Will I build projects?

Yes. Every course includes practical labs and one or more projects designed to help you demonstrate the skills you learn.

Is a certificate included?

Yes. A completion certificate can be issued after meeting attendance, assessment and project requirements.

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