Neural Network Course
This course provides hands-on training in developing neural network models for tabular classification, covering the full pipeline from data preparation to deployment.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This Neural Network Course teaches you how to build, train, and deploy accurate tabular classification models using real user-level data. You will learn data ingestion, feature engineering for mixed categorical and numeric inputs, robust splitting strategies, hyperparameter tuning, and practical optimization. The course also covers evaluation, interpretation, calibration, and production-readiness so you can ship reliable, high-impact models fast.
Elevify advantages
Develop skills
- Design tabular neural nets: build MLPs, embeddings, and calibrated outputs fast.
- Tune training like a pro: optimize hyperparameters, schedulers, and batch sizes.
- Engineer powerful features: encode mixed data, scale numerics, and craft signals.
- Build robust ML pipelines: split data correctly, handle imbalance, and log runs.
- Ship production models: interpret metrics, explain predictions, and plan A/B tests.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
Your classes are perfect. I purchased the one-year package and finally have the chance to follow several topics I'm interested in without having to switch platforms... I appreciate everything you do, I've already recommended you to others...

Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.

Mariana FerresPhotography Student
I like the content and the way the videos are presented and transcribed, which speeds up the process!

Luciana AlvarengaNail Design Student
The platform is fast and easy to use. The variety of content and the extra videos really help with learning.

Giulio CarloPrompt Engineering Student
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