Microsoft Cloud Skills: Machine Learning Challenge

Table of Contents

Machine Learning Challenge

Machine learning is at the core of artificial intelligence, and many modern services depend on predictive machine learning models. Learn how to use Azure Machine Learning to create and publish models without writing code. You’ll also explore the various developer tools you can use to interact with the workspace.

I participated to the learning challenge in mid summer but I wanted to create this post so IF I don’t manage to get this done before August 14 2023 (because I’m on vacation)  I can always return to it and of course everyone else can use this too to learn.

I don’t know if Microsoft are going to leave this challenge as is. 

Link to the Machine learning challenge is  (opens to the new tab).

NOTE. Every image in this post is created with AI service.

 

** UPDATE Aug 14th, 2023:

I completed the challenge.

Modules for this challenge

There are 13 modules in this challenge. Read and learn. Some of those modules prepares you for the Microsoft Azure AI Fundamentals certification exam.

NOTE. Headers include the links to the topics.

Get started with AI on Azure

With AI, we can build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.

In this module, you’ll learn about the kinds of solution AI can make possible and considerations for responsible AI practices.

Introduction to GitHub Copilot

GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor.

In this module, you will:

  • Understand how GitHub Copilot can help you code by offering autocomplete-style suggestions
  • Enable GitHub Copilot for Business for your enterprise
  • Learn how to configure GitHub Copilot
  • Troubleshoot GitHub Copilot

Introduction to machine learning

A high-level overview of machine learning for people with little or no knowledge of computer science and statistics. You’ll be introduced to some essential concepts, explore data, and interactively go through the machine learning life-cycle – using Python to train, save, and use a machine learning model like we would in the real world.

In this module, you will:

  • Explore how machine learning differs from traditional software
  • Create and test a machine learning model
  • Load a model and use it with new data

Use Automated Machine Learning in Azure Machine Learning

Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.

Learn how to use the automated machine learning user interface in Azure Machine Learning

Create a regression model with Azure Machine Learning designer

Regression is a supervised machine learning technique used to predict numeric values. Learn how to create regression models using Azure Machine Learning designer.

Learn how to train and publish a regression model with Azure Machine Learning designer.

Create a classification model with Azure Machine Learning designer

Classification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.

Train and publish a classification model with Azure Machine Learning designer.

Create a clustering model with Azure Machine Learning designer

Clustering is an unsupervised machine learning technique used to group similar entities based on their features. Learn how to create clustering models using Azure Machine Learning designer.

Train and publish a clustering model with Azure Machine Learning designer.

Explore and analyze data with Python

Data exploration and analysis is at the core of data science. Data scientists require skills in programming languages like Python to explore, visualize, and manipulate data.

Train and evaluate regression models

Regression is a commonly used kind of machine learning for predicting numeric values.

In this module, you’ll learn:

  • When to use regression models.
  • How to train and evaluate regression models using the Scikit-Learn framework.

Train and evaluate classification models

Classification is a kind of machine learning used to categorize items into classes.

In this module, you’ll learn:

  • When to use classification
  • How to train and evaluate a classification model using the Scikit-Learn framework

Train and evaluate clustering models

Clustering is a kind of machine learning that is used to group similar items into clusters.

In this module, you’ll learn:

  • When to use clustering
  • How to train and evaluate a clustering model using the scikit-learn framework

Train and evaluate deep learning models

Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.

In this module, you will learn:

  • Basic principles of deep learning
  • How to train a deep neural network (DNN) using PyTorch or Tensorflow
  • How to train a convolutional neural network (CNN) using PyTorch or Tensorflow
  • How to use transfer learning to train a convolutional neural network (CNN) with PyTorch or Tensorflow

Refine and test machine learning models

When we think of machine learning, we often focus on the training process. A small amount of preparation before this process can not only speed up and improve learning, but also give us some confidence about how well our models will work when faced with data we have never seen before.

In this module, you will:

  • Define feature scaling.
  • Create and work with test datasets.
  • Articulate how testing models can both improve and harm training.

Final words

I think it’s good that Microsoft creates these challenges with (or without) prizes because it encourages people to learn.

Learning is the key for your future.

In IT business you have to learn something everyday so you don’t fall by the wayside (in finnish pudota kelkasta) or something like that. 

And remember these are FREE.

Image was made with DALL-E and post-edit with Adobe Photoshop Generative Fill.
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Jussi Metso
Author is a a lifelong IT enthusiast, Microsoft Security MVP and interested in Cloud Security, XDR, SIEM and AI. Motto: Learning is the key for your future.

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