Course Outline
Introduction
Setting up a Working Environment
Installing Auto-sklearn
Anatomy of a Standard Machine Learning Workflow
How Auto-sklearn Automates the Machine Learning Workflow
Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)
Case Study: AutoML with Auto-sklearn
Downloading a Dataset
Building a Machine Learning Model
Training and Testing the Model
Tuning the Hyperparameters
Building, Training, and Testing Additional Models
Tweaking the Hyperparameters to Improve Accuracy
Configuring Auto-sklearn for Deep Learning Models
Troubleshooting
Summary and Conclusion
Requirements
- Experience with machine learning algorithms.
- Python programming experience.
Audience
- Data scientists
- Data analysts with a technical background
Delivery Options
Private Group Training
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- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
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Public Training
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