BigML Training Workshop

Welcome to the BigML training workshop!

The overall objective of this workshop is to give you useful training resources for a complete introduction to all of the features that BigML has to offer in a practical hands-on format. The training resources available on this page are suitable for everyone with all types of skill levels, from absolute beginner to advanced practitioner, to practice Machine Learning with BigML in a self-study manner.

If you would prefer a BigML expert to guide you in this journey and prepare you with everything you need to be successful with your own Machine Learning projects, please contact us at education@bigml.com to request your training course with a BigML lecturer.

Book your live course

Topics included

  • What is Machine Learning, why and when is it needed.
  • What kinds of machine learning tasks are there and how to use them effectively.
  • Validating your machine learned models, and techniques to improve them.
  • Collecting, transforming, and preparing data for machine learning.
  • Deploying a machine learned model into a production system.
  • Workflows that combine machine learned models to make real-world applications.
  • Automating a machine learning workflow to maximize the time value of your data.

Objectives

First things first! Register for an account

If you already have a BigML account please sign in here.

register

Objectives

Supervised learning is a type of machine learning concerned with building models that can make predictions.

In this block you will learn:

  • What is supervised learning, why and when is it needed.
  • How to evaluate machine learning models and to improve them.
  • What kinds of supervised learning models are supported by BigML.
  • How each algorithm works, specifically with regards to it's advantages or disadvantages.
  • How to automate model selection and tuning for optimal performance.
  • A real-world example explaining how to go from idea to outcome and the challenges of machine learning applications.

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Objectives

Unsupervised learning is a type of machine learning which makes working with unlabelled data possible and includes tasks such as clustering, anomaly detection, and association discovery.

In this block you will learn:

  • What is unsupervised learning and why it is important.
  • Which unsupervised alogrithms are available in BigML.
  • How each algorithm works, specifically with regards to it's application and interpretation.
  • Combining unsupervised methods into real-world application workflows.
  • A real-world example explaining how to go from idea to outcome and the challenges of machine learning applications.

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