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ML Engineering Course
Master the essential aspects of machine learning engineering with our comprehensive ML Engineering Course. Designed for tech professionals, this course covers everything from data collection and preprocessing right through to model training, evaluation, and deployment in a production environment. Get stuck in with recommendation systems, explore machine learning algorithms, and learn how to integrate real-time predictions with e-commerce platforms. Gain practical skills in scalability, documentation, and reporting, ensuring you're properly equipped for real-world applications.
- Deploy models: Master scalability and real-time predictions in a production setup.
- Build recommendation systems: Explore different types and their applications for e-commerce.
- Implement ML algorithms: Use hybrid, content-based, and collaborative methods.
- Train and evaluate models: Learn about precision, recall, and F1-score metrics.
- Preprocess data: Identify data sources, encode, normalise, and handle missing data properly.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Master the essential aspects of machine learning engineering with our comprehensive ML Engineering Course. Designed for tech professionals, this course covers everything from data collection and preprocessing right through to model training, evaluation, and deployment in a production environment. Get stuck in with recommendation systems, explore machine learning algorithms, and learn how to integrate real-time predictions with e-commerce platforms. Gain practical skills in scalability, documentation, and reporting, ensuring you're properly equipped for real-world applications.
Elevify advantages
Develop skills
- Deploy models: Master scalability and real-time predictions in a production setup.
- Build recommendation systems: Explore different types and their applications for e-commerce.
- Implement ML algorithms: Use hybrid, content-based, and collaborative methods.
- Train and evaluate models: Learn about precision, recall, and F1-score metrics.
- Preprocess data: Identify data sources, encode, normalise, and handle missing data properly.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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