Neural Network has become a crucial part of modern technology. It has influenced our daily life in a way that we have never imagined. From e-commerce and solving classification problems to autonomous driving, it has touched everything. In this tutorial, we are gonna discuss all the important aspects of neural networks in the simplest way possible and at the end of the tutorial, we have also provided **code** implemented in **python** for all the described parts.

Animal brains even the small ones like the brain of a pigeon was more capable of than digital computers with huge processing power and…

Linear Classifiers are one of the most commonly used classifiers and Logistic Regression is one of the most commonly used linear classifiers. The concepts we are going to learn here will actually extend a lot of other classification methods beyond linear classifiers. We’re going to learn the fundamental concepts, but also the underlying algorithms that let us optimize the parameters of this model to fit your training data.

Previously, we have discussed briefly the simple linear regression. Here we will discuss **multiple regression** or multivariable regression and how to get the solution of the multivariable regression. At the end of the post, we will provide the **python code** from scratch for multivariable regression.

A single variable linear regression model can learn to predict an output variable y when there is only one input variable, x and there is a linear relationship between y and x, that is, y≈w₀+w₁*x. Well, that might not be a great predictive model for most cases. For example, let’s assume we are going to…

Regression is a very fundamental concept in statistics, machine learning and in Neural Network. Imagine plotting the correlation between rainfall frequency and agriculture production in high school. Increase in rainfall generally increases agriculture production. Fitting a line to those points enables us to predict the production rate under different rain conditions. It was actually a very simplest form of linear regression.

In simple words, regression is a study of how to best fit a curve to summarize a collection of data. It’s one of the most powerful and well-studied types of supervised learning algorithms. In regression, we try to understand…

To build an accurate machine learning model we need to have a proper understanding of the error. In forming predictions of a model there are three sources of error: **noise**, **bias**, and **variance**. Having proper knowledge of error and bias-variance would help us building accurate models and avoiding mistakes of overfitting and underfitting.

In this tutorial, our case study will be how to predict house prices. We have a dataset which consists of house prices with the square feet of the house associated with it.

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