From the course: Microsoft Azure AI Essentials: Workloads and Machine Learning on Azure

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Overview of machine learning

Overview of machine learning

From the course: Microsoft Azure AI Essentials: Workloads and Machine Learning on Azure

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Overview of machine learning

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- [Instructor] Machine learning is an AI technique that uses mathematical algorithms to create predictive models. We give an algorithm example data, and it learns by finding patterns to generate models. Those models are used to make predictions or decisions about new data. Now, do you ever wonder how this works behind the scenes? Let's say a farmer wants to use machine learning to predict crop yield. First, the farmer gathers data such as rainfall, temperature, soil, humidity, elevation, sunlight, and even chemicals like nitrogen, phosphorus, potassium, and sulfur. In machine learning, we call these features of the model, represented as x. The farmer also measures crop yield based on these features. This would be called the label in machine learning, represented as y. The farmer tracks these observations for a full year, considering seasonality, and places them in a dataset. Once the data is collected, it's split into a training set, usually 70 to 80% of the data, and a validation…

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