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Neural Network Course

Neural Network Course
flexible workload of 4 to 360h
valid certificate in your country

What will I learn?

This Neural Network Course shows 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
Workload: between 4 and 360 hours

What our students say

Your classes are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of my interest without needing to change platforms... I thank you for everything you do, I've already recommended you to other people...
Giulio Carlo
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 Ferres
Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos really help with learning.
André Felipe
André FelipePrompt Engineering Student

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