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IT Training

Building your own Models with Azure Machine Learning

In many AI solutions pre-trained models (such as chat-GPT) are used. But in some cases, custom models trained on your corporate data is essential to make predictions within your business processes. This training focusses on the latter. First you will learn how to train models locally using Python. Then the focus shifts on how Azure Machine Learning provides extra features, such as scale out training, easy deployment and monitoring.

Who should attend this course?

This training aims at people with basic Python knowledge who want to start or further grow in a data science role. No machine learning knowledge is needed, but basic Python data manipulation skills are required to take the labs. If needed, attend our Python for data engineering training first.

Prerequisites

    Machine Learning Introduction

  • Before ML can be applied, the key concepts of machine learning need to be discussed.

    • Setup your environment

  • Before you can start developing machine learning solutions, you must first create an environment in which you can store and run your Python code. Both local and cloud solutions are presented.

    • Prepare your data for Machine Learning

  • Like all data used for business intelligence, also data for machine learning should be cleansed. But machine learning data has some special requirements. This module shows how to use Python to prepare your data for machine learning.

    • Build your own Machine Learning Model with scikit-learn

  • Many business problems can be tackled by basic machine learning techniques. In this module the most common machine learning approaches such as linear regression and random forests are used with Python. We also pay attention to model inspection.

    • Azure Machine Learning

  • Machine learning on a local machine and a small dataset is one thing, running this on larger datasets or more CPU-hungry techniques (or even GPUs) can become a challenge. Another problem is deploying your model: How can we easily call the resulting model from within other applications? Azure Machine Learning helps answering these questions.

    • Microsoft Fabric Data Science

  • The Data Science experience in Microsoft Fabric provides notebooks, so all the previous Python code could be run in their. But Microsoft Fabric provides extra functionality as well: From pre-installed machine learning frameworks (SparkML, FLAML) to model management (MLFlow).

    • Automated Machine Learning

  • Part of the machine learning process can be automated. Azure Automated machine learning provides a web portal and a Python API to automate data preprocessing and model training.

    • Using Keras for Deep Learning

  • From all the machine learning techniques there is one that gets popular for more challenging problems: Multiple layers of neural networks, better known as deep learning. For problems such as image recognition, speech understanding etc. this is currently the way to go. But it’s from a mathematical point of view a very challenging technique. In this module the basics of deep learning are introduced.

Practical information

Duration

Publish

Languages

EN

Price

€ 1.400 + 21% VAT

Location

Classroom Course

Schedule

Guaranteed to run

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