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
Testimonials (5)
The trainer showed that he has a good understanding of the subject.
Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
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
Convolution filter
Francesco Ferrara
Course - Introduction to Machine Learning
The trainer was so knowledgeable and included areas I was interested in.