Hi everyone 👋👋.Hope you’re having a nice weekend 👌.Today we are continuing our journey with the “intro to machine learning” series.And of course, SVMs 🎉.SVM or support vector machine is a machine learning algorithm whose job is to find a hyperplane that divides the data for the each label 👍.The power of SVMs comes through especially when the data is non linear or not easily distinguishable by eye!.SVMs use non linear kernels to transform the data to another space where the resulting data points are easily divisible by a hyperplane and then they transform everything including the hyperplane back to the initial dimensions.How cool is that with the right kernel, the SVM in the post was able to figure out the circular pattern of our data points? 👏👏.I personally think it’s absolutely amazing!!.Let me know what you think below.

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👨‍💻 #Machine_Learning

What do you say we learn some machine learning today? 👌🎉.Linear regression is typically one of the first machine learning algorithms we hear about when we start learning about ML in general.Disguised in a simple intuitive algorithm, there exists a series of foundational concepts in ML 🤔.To find a loss function whose minimization gives the problem the optimal result.To visualize that loss function and how to minimize it 👌👌.Did you know that companies such as Zillow and Redfin have used a flavor of Linear Regression to predict prices of homes?!! 😱
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👨‍💻 #Machine_Learning