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Course Outline
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
Requirements
- Experience with machine learning algorithms.
- Python or R programming experience.
Audience
- Data analysts
- Data scientists
- Data engineers
- Developers
14 Hours
Testimonials (6)
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
The Exercises
Khaled Altawallbeh - Accenture Industrial SS
Course - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
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
Course - Applied AI from Scratch in Python
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course - Applied AI from Scratch in Python
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
