Skip to product information
1 of 1

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.
View full details