Machine learning is a broad field that encompasses many different techniques and algorithms, but many of these techniques are indeed based on linear regression . Think of it this way: Imagine you have a complex problem, like predicting the stock price of a company. You might break down this problem into smaller, simpler problems that can be solved using linear regression. For example, you might try to predict the stock price based on factors such as the company's earnings, the overall state of the economy, and recent news about the company. Each of these factors can be modeled using a separate linear regression, and the outputs from these regressions can then be combined to make a prediction about the stock price. So, in this sense, machine learning can be thought of as a collection of many linear regressions working together to solve a more complex problem. The idea is that by breaking down a complex problem into simpler parts and modeling each part with a separate regression, the
Machine learning algorithms can be thought of as a set of instructions for a computer to follow, just like a recipe. But instead of telling the computer exactly what to do step by step, we give it a goal and some data, and it tries to figure out the steps to reach that goal on its own. This process of the computer figuring out the steps on its own is what we mean when we say "a machine is learning." Here's an analogy to help explain it better: Let's say you want to teach your friend to bake a cake. You could give them a recipe with precise instructions, like "Mix 2 cups of flour, 1 cup of sugar, 1/2 cup of butter, etc." But this method is inflexible, and your friend wouldn't know how to make the cake if you changed the recipe or ingredients. Alternatively, you could give your friend the goal of "baking a cake that tastes good," and show them a bunch of cake recipes and ingredients. Your friend would then use their own judgment and experimentati