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ML Engineering Course
Become a master of the important things in machine learning engineering with our full ML Engineering Course (Mu Zambia). This course is made for people who work in technology and it will teach you everything from getting data and cleaning it up, to training models, checking how good they are, and putting them to work. We will look at recommendation systems, learn about machine learning algorithms, and how to use real-time predictions with online shops. You will get skills in making things bigger, writing documentation, and making reports, so you are ready to use what you have learnt in the real world.
- Put models to work: Learn how to make them bigger and use real-time predictions.
- Build recommendation systems: Look at different types and how they are used in online shops.
- Use ML algorithms: Use different ways of doing things, like hybrid, content-based, and collaborative methods.
- Train and check models: Learn how to use precision, recall, and F1-score to see how good they are.
- Clean up data: Find where it comes from, encode it, make it normal, and deal with missing data.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Become a master of the important things in machine learning engineering with our full ML Engineering Course (Mu Zambia). This course is made for people who work in technology and it will teach you everything from getting data and cleaning it up, to training models, checking how good they are, and putting them to work. We will look at recommendation systems, learn about machine learning algorithms, and how to use real-time predictions with online shops. You will get skills in making things bigger, writing documentation, and making reports, so you are ready to use what you have learnt in the real world.
Elevify advantages
Develop skills
- Put models to work: Learn how to make them bigger and use real-time predictions.
- Build recommendation systems: Look at different types and how they are used in online shops.
- Use ML algorithms: Use different ways of doing things, like hybrid, content-based, and collaborative methods.
- Train and check models: Learn how to use precision, recall, and F1-score to see how good they are.
- Clean up data: Find where it comes from, encode it, make it normal, and deal with missing data.
Suggested summary
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