From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

Introducing AI and machine learning algorithms

From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

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Introducing AI and machine learning algorithms

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- Let's say that we're baking cookies. We have our ingredients, flour, sugar, butter, and chocolate chips. We then need to figure out how to combine them together into the final product. To get there, we'll follow the steps in a recipe. Now, the great thing about using recipe is that we expect to see the same output each time we make it, unless of course, we change something or a piece of the recipe is missing. Much like a recipe, an algorithm's a series of steps to solve a problem. You probably use algorithms all the time in your own work, regardless of whether that's in Excel, Python, or any other tool. An algorithm could be as simple as converting a temperature value from degrees Fahrenheit to degrees Celsius and vice versa. In using AI algorithms on data, we're looking for trends, groups, or outliers and anomalies. Algorithms have finite sequences of steps. We can apply the logic of these steps to find a solution. We can then quantitatively compare these solutions. We repeat these steps to solving an algorithm until we arrive at the optimized solution for an AI algorithm or machine learning model, for example. While we can solve these algorithms using Excel, either manually or with the help of the built-in solver, add-in, using Python instead here can greatly streamline our modeling process. It's important to emphasize that this isn't a course focusing on the details of how a specific algorithm or visualization works. We focus on examples of how Python code can help us run algorithms or create visuals that aren't otherwise possible in the built-in functionalities of Excel. We want to integrate Python into Excel, not replace it. Algorithms are often used together with many others in technical ecosystems like social media algorithms. Recommendation systems, for example, can filter, rank, and select the content shown to users. They can then make decisions of their own with these recommendations. In Excel, we're not going to be building out large recommendation systems because there are limitations with working with data points within the application. We can, however, understand how much smaller pieces of it work by breaking them into components that we can play with in Excel. Now that we know what algorithms are, let's get started building them in Excel with Python.

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