Learn Data Science Easy Way A To Z

What is Gradient Descent? Gradient descent is an efficient first-order optimization algorithm for finding a differentiable function's global or local minimum. It estimates the values of parameters or coefficients that minimize a cost function.  The gradient descent method has proved to be especially useful as it can be adopted in spaces of any number of dimensions. The gradient descent method can be used when parameters cannot be calculated analytically and is a good choice for the differentiable cost function. How does Gradient Descent work? Image Credit: Neural Networks and Deep Learning To get an intuitive idea of how Gradient Descent works, let us consider the entire range of values the parameters can take. Here the axes w and b represent the range of values the parameters w and b can take, respectively. In this case, these parameters express a simple linear unit. Hence, the curved surface shown represents the cost function J(w, b) would vary for different values of w and ...

 Regression metrics help us evaluate the performance of regression models in machine learning. For beginners, understanding these parameters is important for model selection and optimization. In this article, we will focus on the important regression metrics: MAE, MSE, RMSE, R² score, and adjusted R² score.   Each section is written in list format for better clarity and understanding.   1. Mean Absolute Error (MAE)   MAE calculates the average of absolute differences between predicted and actual values.   formula:      Important points:   1. Easy to understand: MAE is easy to understand and calculate.   2. Same unit as the target variable: The errors are in the same unit as the target variable.   3. Not sensitive to outliers: Large errors do not affect MAE as much as they do MSE.   Use cases:   When you need a simple and descriptive metric for error measurement.   Python code:   import mean_absolute_error fr...

Simple linear regression is one of the most important techniques in data science and machine learning. It is the foundation of many statistical and machine learning models. Even though it is simple, its concepts are widely applicable in predicting outcomes and understanding relationships between variables. This article will help you learn about: 1. What is simple linear regression and why it matters. 2. The step-by-step intuition behind it. 3. The math of finding slope() and intercept(). 4. Simple linear regression coding using Python. 5. A practical real-world implementation. If you are new to data science or machine learning, don’t worry! We will keep things simple so that you can follow along without any problems. What is simple linear regression? Simple linear regression is a method to model the relationship between two variables: 1. Independent variable (X): The input, also called the predictor or feature.  2. Dependent Variable (Y): The output or target value we want to predi...