AutoML - Automated Machine Learning
AutoML - Automated Machine Learning
The course on addresses the challenge of designing well-performing Machine Learning (ML) pipelines, including their hyperparameters, architectures of deep Neural Networks and pre-processing. Future ML developers will learn how to use and design automated approaches for determining such ML pipelines efficiently.
Which topics will be covered?
- In Hyperparameter Optimization, the hyperparameter settings of a given Machine Learning algorithm are optimized to achieve great performance on a given dataset.
- In Neural Architecture Search, the architecture of a Neural Network is tuned for its predictive performance (or in addition inference time or model size) on a given dataset.
- As AutoML optimizers, approaches such as Bayesian optimization, evolutionary algorithms, multi-fidelity optimization and gradient-based optimization are discussed.
- Via Dynamic & Meta-Learning, useful meta strategies for speeding up the learning itself or AutoML are learned across datasets.
What will I achieve?
By the end of the course, you‘ll be able to…
- identify possible design decisions and procedures in the application of ML.
- evaluate the design decisions made.
- implement efficient optimizers for AutoML problems, such as hyperparameter optimization and neural architecture search.
- increase the efficiency of AutoML via a multitude of different approaches.
Which prerequisites do I need to fulfill?
- Basics in Machine Learning (ML) and Deep Learning (DL)
- First experiences in the application of ML & DL
- Python or R as programming language
Duration: 14 weeks
Level: Intermediate
Certification: Yes
Cost: Free
Language: English
Type: Self-Paced
Please note: these courses are provided by external sources, links are not actively managed or regularly updated, content might be moved or unavailable.
