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Automated Machine Learning for Hyperparameter Optimization and Algorithm Selection

Francesca Lazzeri

Automated Machine Learning enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. Automated Machine Learning is seen as a fundamental shift in which organizations can approach making machine learning. In this training, you will learn how to use Automated Machine Learning to automate selection of machine learning models and automate tuning of hyperparameters.

The traditional machine learning model development process is highly resource-intensive and requires significant domain knowledge and time investment to run and compare the results of dozens of models. Automated Machine Learning simplifies this process by generating models tuned from the goals and constraints you defined for your experiment, such as the time for the experiment to run or which models to blacklist.

What you'll learn-and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • How Automated Machine Learning works
  • How to use the libraries and cloud services that support Automated Machine Learning

And you’ll be able to:

  • Generate a forecast machine learning model using Automated machine learning
  • Perform data preprocessing with Automated machine learning
  • Perform algorithm selection with Automated machine learning
  • Perform hyperparameter selection with Automated machine learning
  • Train multiple models on a remote cluster
  • Review training results and register the best model
  • Deploy your model as web service

This training course is for you because...

  • You are a business analyst or a data scientist with machine learning background and you need to apply Automated ML to your machine learning pipelines to improve processes and operations in your company.
  • You work with machine learning developers and your team needs to build multiple machine learning pipelines and easily operationalize machine learning models
  • You want to become a data scientist and you want to learn how and when to use Automated Machine Learning

Prerequisites

  • Experience coding in Python
  • A basic understanding of machine learning and deep learning topics and terminology

Recommended preparation:

Recommended preparation:

Recommended follow-up:

About your instructor

  • Francesca Lazzeri, Ph.D. is Senior Machine Learning Scientist at Microsoft on the Cloud Advocacy team and expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries – energy, oil and gas, retail, aerospace, healthcare, and professional services.

    Before joining Microsoft, she was Research Fellow in Business Economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation.

    Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She is Data Science mentor for Ph.D. and Postdoc students at the Massachusetts Institute of Technology, and speaker at academic and industry conferences - where she shares her knowledge and passion for AI, machine learning, and coding.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

What is automated machine learning? (60 minutes)

  • Presentation: How Automated Machine Learning works (15 minutes)
  • Discussion: Categories of supervised learning for Automated Machine learning (15 minutes)
  • Exercise: in Python notebooks (15 minutes)
  • Q&A (10 minutes)
  • Break (5 minutes)

Use automated machine learning to build your model (60 minutes)

  • Presentation: How to use automated machine learning to build your model (15 minutes)
  • Discussion: Define settings for auto-generation and tuning (15 minutes)
  • Exercise: in Python notebooks (15 minutes)
  • Q&A (10 minutes)
  • Break (5 minutes)

Use automated machine learning to deploy your model (60 minutes)

  • Presentation: How to use automated machine learning to deploy your model (15 minutes)
  • Discussion: Register, test and operationalize your model (15 minutes)
  • Exercise: in Python notebooks (15 minutes)
  • Q&A (15 minutes)