References: Microsoft Learn – Machine Learning Fundamental Concepts

References: Microsoft Learn
I firmly believe that the chapter would have prepared you for your AI-900 certification. Before we move on to the next chapter and learn more about computer vision, I strongly suggest that you go through the various modules of Azure AI Fundamentals: Explore visual tools for machine learning in Microsoft Learn, using the following links:

1. https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine-learning-designer/
2. https://learn.microsoft.com/en-us/training/modules/create-classification-model-azure-machine-learning-designer/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.create-no-code-predictive-models-with-azure-machine-learning
3. https://learn.microsoft.com/en-us/training/modules/create-clustering-model-azure-machine-learning-designer/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.create-no-code-predictive-models-with-azure-machine-learning
4. https://learn.microsoft.com/en-us/training/modules/use-automated-machine-learning/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.create-no-code-predictive-models-with-azure-machine-learning

Summary
Machine learning is the backbone of most AI solutions. It makes it possible to make models that can predict unknown values and draw conclusions from data they have already seen.
Massive datasets are used by data scientists, ML engineers, and AI experts and are broken down into training, testing, and validation sets. The most important parts of machine learning are getting and processing data, making and training a model, validating the model’s output, iterating the model until the end goals are met, and putting the model online as a service.
Supervised machine learning and unsupervised machine learning are two of the most common ways to use machine learning. Supervised machine learning makes use of a dataset that has been labeled, whereas unsupervised machine learning works with data that is unknown. Clustering is an example of unsupervised learning. Regression and classification, on the other hand, are examples of supervised learning.
People use Azure Machine Learning to manage the whole life cycle of machine learning projects, from designing machine learning apps to training, validating, and quickly deploying them. Infrastructure security and role-based access control are things that businesses that use Microsoft Azure Machine Learning will be familiar with.
In the next chapter, you will learn about the computer vision field of artificial intelligence. You will also learn about the speech, language, vision, decision, and OpenAI cognitive services that Azure offers. Also, the chapter talks about different kinds of vision services, such as computer vision, custom vision, face recognition, form recognition, and optical character recognition (OCR).