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.
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.
First things first! Register for an account
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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Extra Educational Videos
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Related Educational Videos
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Related Educational Videos
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.
