Summary  
This chapter covers data preprocessing, constructing and training a neural network, and evaluating its performance in code.  

General domain of usage  
Image classification in computer vision

Welcome to a **Python project** focusing on **recognizing handwritten digits** using **machine learning algorithms**. Handwritten digit recognition is a pivotal challenge in **computer vision**, boasting a wide array of practical applications, including **digitizing documents**, **recognizing zip codes** in postal addresses, and **authenticating bank checks**. Throughout this project, we will leverage the **Python programming language** and key libraries such as **`NumPy`**, **`pandas`**, and **`tensorflow`** to craft a model proficient in accurately identifying handwritten digits.

The endeavor will encompass various phases, namely **preprocessing the data**, **constructing a neural network**, **training the model**, and **assessing its efficacy**. We will utilize the renowned **MNIST dataset**, renowned for its extensive compilation of handwritten digit images and corresponding labels. Our ambition is to engineer a model that excels in deciphering the digits depicted in these images with remarkable precision.

In our journey, we will delve into the realm of advanced machine learning techniques and algorithms.

In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms. This endeavor aims to harness the power of machine learning to effectively interpret and understand handwritten digits, showcasing the potential of these algorithms in processing and analyzing complex visual information.

In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms.