![]() ![]() ![]() For instance, the housing price and the size in square meter are features. These characteristics of one date (house) in a data set (houses) are called features. If you think about the problem, what would be the characteristics of a house in an urban area to predict its price? The size? The year when it was built? The distance to the city centre? The point is that there are endless of characteristics for a house that could contribute to the price of a house. A common problem to solve when learning ML is predicting housing prices in Portland. Solving a Problem with Machine Learningīy using machine learning (ML), we want to solve a real problem. Furthermore, I am just learning it myself, so if there are any mistakes on the way, please help me out. The article doesn't give you an in depth explanation of linear regression and gradient descent, so if you are interested in those topics, I highly recommend the referenced machine learning course. Afterward, I hope to find the time to transition these learnings to Python. Since JavaScript is the programming language that I feel most comfortable with, I try to apply my learnings in machine learning in JavaScript as long as I can. In the following article, I want to guide you through building a linear regression with gradient descent algorithm in JavaScript. Now, it is refreshing to see use cases in machine learning where those learnings could be used. Starting right into web development after university, I never had the opportunity to apply those learnings when implementing web applications. ![]() So far, it is a blast and I am so keen to apply all my learnings in math from university. Recently I started to take the Machine Learning course by Andrew Ng on Coursera. ![]()
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