Summary  
A neural network is built from interconnected layers of neurons with weighted connections, and training adjusts these weights to improve the model’s predictive accuracy.

General domain of usage  
House price prediction

## Neural Network Structure

A neural network is a structure made up of **layers of "neurons"**, similar to biological neurons in the brain. Each neuron processes information, receives **input**, and **passes the result on** to the next layer. The image below illustrates a simple **artificial neural network (ANN)** with three layers: **input, hidden, and output**.  

- The **input layer** receives data;  
- The **hidden layer** processes information through weighted connections;  
- The **output layer** produces the final result.  

Like learning a language, the network refines its understanding through repeated exposure to data, recognizing patterns, and improving predictions.


Neurons in a neural network are connected by **weighted connections**, where each **weight** represents the importance of the link between two neurons. As shown in the image, **each neuron in one layer is connected to every neuron in the next layer**, allowing information to flow through the network.

The thicker the connection, the more important it is.

Note

The process of training a neural network involves **adjusting the weights** of its neurons so that the output becomes as accurate as possible. It is similar to learning to play a musical instrument — gradual practice leads to improved precision and performance.

However, it is important to remember that neural networks are only a tool — they **do not possess consciousness** or an understanding of the world like humans do. They simply process data and detect patterns they were trained to recognize. For example, a neural network trained to predict house prices would not be able to predict the price of a guitar in a music store.


Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.