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Author: Wendy Bryant


Cleaning – Machine Learning Fundamental Concepts

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Cleaning Figure 3-20Adding the Clean Missing Data module Figure 3-21Customizing the Clean Missing Data module Figure 3-22Configuring the Clean Missing Data module Training Our Model Figure 3-23Adding the Split Data module Figure 3-24Splitting 70% data for training and 30% data for testing Figure 3-25Adding the Train Model component Figure 3-26Rearranging the tabs Figure 3-27Editing the […]

Scoring Model – Machine Learning Fundamental Concepts

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Scoring Model Figure 3-29Adding and configuring the scoring model Evaluation Figure 3-30Adding and configuring the evaluation model Submission Figure 3-31Running our machine learning model Figure 3-32Naming the experimentWe see “Completed” marks in some tabs and “Running” in others as shown in Figure 3-33. Figure 3-33We can see the completed modules and running modules as the […]

Exploration – Machine Learning Fundamental Concepts

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ExplorationThe “Usage + Quotas” section of the ML workspace shows the dedicated core usage as shown in Figure 3-39. Figure 3-39Exploring usage + quota Delete ResourcesTo avoid any charges, it is critical to delete the resources once the ML model is no longer needed. Figure 3-40 shows Delete button to delete the ML model. Figure […]

References: Microsoft Learn – Machine Learning Fundamental Concepts

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References: Microsoft LearnI 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 […]

Introducing Azure Machine Learning – Machine Learning Fundamental Concepts

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Introducing Azure Machine Learning Developers can build, deploy, and improve high-quality machine learning models with Azure’s machine learning tools and user-friendly platform. It adds more value to a project by reducing the time to value with industry-leading machine learning operations (MLOps), open source interoperability, and integrated tools. This trusted platform is designed for responsible AI […]

Tools for Azure Machine Learning – Machine Learning Fundamental Concepts

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Tools for Azure Machine Learning Azure Machine Learning offers various tools to build your ML solutions. Anyone on an ML team can use their preferred tools to get the job done. Azure Machine Learning Studio The Microsoft Azure Machine Learning Studio is the main place where machine learning services in the Microsoft Azure Cloud are […]

What Is Automated Machine Learning? – Machine Learning Fundamental Concepts

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What Is Automated Machine Learning? Using automated machine learning, models can be easily created with no coding required. The most complicated models are hard to make, but they are useful for figuring out how well a standard model works for the first time or comparing different models. You have complete control over the primary metric, […]

Create Azure Machine Learning Workspace – Machine Learning Fundamental Concepts

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Create Azure Machine Learning Workspace   1. Click “+ Create a resource” and then search for “machine learning” to provision an Azure Machine Learning resource as shown in Figure 3-4.   Figure 3-4Creating a new Azure ML resource   2. As shown in Figure 3-5, fill in the information on the “Basic” tab, and on […]

Model Evaluation Metrics – Machine Learning Fundamental Concepts

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Model Evaluation Metrics Model evaluation is the process of figuring out how well a machine learning model works and what its strengths and weaknesses are by using different evaluation metrics. Since the results are one of the most important parts of any model, we should always look at them after each testing cycle. From our […]

The Two Classes of Supervised Machine Learning – Machine Learning Fundamental Concepts

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The Two Classes of Supervised Machine Learning Regression Basically, regression is a model that was made to find the relationship between the independent variable (features) and the dependent variable (output). In machine learning, it is used for predictive modeling, where an algorithm anticipates a series of continuous outcomes. Its result is a continuous numerical value, […]