Plan Szkolenia
Introduction
Setting up a Working Environment
Overview of AutoML Features
How AutoML Explores Algorithms
- Gradient Boosting Machines (GBMs), Random Forests, GLMs, etc.
Solving Problems by Use-Case
Solving Problems by Training Data Type
Data Privacy Considerations
Cost Considerations
Preparing Data
Working with Numeric and Categorical Data
- IID tabular data (H2O AutoML, auto-sklearn, TPOT)
Working with Time Dependent Data (Time-Series Data)
Classifying Raw Text
Classifying Raw Image Data
- Deep Learning and Neural Architecture Search (TensorFlow, PyTorch, Auto-Keras, etc.)
Deploying an AutoML Method
A Look at the Algorithms Inside AutoML
Ensembling Different Models Together
Troubleshooting
Summary and Conclusion
Wymagania
- Experience with machine learning algorithms.
- Python or R programming experience.
Audience
- Data analysts
- Data scientists
- Data engineers
- Developers
Opinie uczestników (5)
Ćwiczenia praktyczne.
Adam Borowski - NetWorkS! Sp. z o.o.
Szkolenie - AI Awareness for Telecom
Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K
Szkolenie - Artificial Intelligence Overview
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Szkolenie - Applied AI from Scratch in Python
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Szkolenie - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day