These concepts form the foundation of many machine learning algorithms. Initially, I decided against writing an article on these topics because they are so widely covered. However, I have changed my mind because understanding these concepts is essential for understanding more advanced topics like Neural Networks (that I plan on tackling in the near future). In addition, this series will be divided into two parts to make it more manageable and organized for better understanding. So make yourself comfortable, grab a cup of coffee, and get ready to embark on a magical journey of machine learning. As with any machine learning problem, we begin with a specific question we want to answer.

In simple language, the gradient descent is an ML optimization strategy which facilitates the ML model to find the minimum loss (cost) function which relates to the optimal variable parameters. Let's understand the concept in detail by applying it on one of the most regression algorithms that ML engineers and Data scientists use, that is Linear Regression. For ease of understanding Gradient descent, we will use simple or univariate linear regression. Here, we are focusing on determining the relationship between one independent variable and one dependent variable(target variable). A univariate linear regression is mathematically represented by y mx c, where'y' is the dependent variable and'x' is the independent or target variable.

You see that our intercept is 6.0398 and our slope or the coefficient for our x is 0.0934. These are the parameters for the 0.5th quantile of our y. Similarly we can do the models for other quantiles. In side the for loop we build models for each quantile in our list quantiles. As we build these models we us also store the model parameters in a list called params.