Algorithm Tutorials

In this lab, you will learn how to evaluate a scikit-learn classification model using various metrics, including accuracy, confusion matrix, precision, recall, and F1 score.

In this lab, you will learn how to build a simple linear regression model using scikit-learn to predict California housing prices.

In this lab, you will learn how to use scikit-learn to build a K-Nearest Neighbors (KNN) classifier, train it on the Iris dataset, and make predictions on new data.

In this lab, you will learn how to verify your scikit-learn installation, import necessary modules, and load a sample dataset to get started with machine learning in Python.

In this lab, you will learn the fundamentals of loading and exploring datasets in scikit-learn using the classic Iris dataset. You will practice accessing data, targets, and feature names, and perform a simple visualization.

In this lab, you will learn the fundamental data preprocessing techniques in scikit-learn, including feature scaling with StandardScaler and target encoding with LabelEncoder, using the classic Iris dataset.

In this lab, you will learn how to perform cross-validation using scikit-learn to evaluate the performance of a machine learning model more robustly.

In this challenge, we will be tasked with finding all the clone soldiers in a clone army parade formation. The clone soldiers are uniquely identified by a number within a specific range, and they can be represented by a square matrix. Our goal is to count the number of clones for each ID and return the statistical result in a dictionary format. The solution should be implemented in the count_clone_soldier(matrix: List[List[str]]) method in the count_clone_soldier.py file.

In this challenge, you will practice using the NumPy module in Python and work with NumPy arrays to perform common mathematical operations.

This tutorial will teach you the basics of working with images using the OpenCV-Python library. By the end of this tutorial, you will be able to read, display, and save images.

In this lab, you will learn how to upgrade a simple shell to an interactive shell within Nmap. Explore techniques using Python and Socat to achieve a full interactive shell, enhancing your penetration testing and network analysis skills.

In this lab, you will master IP subnetting and binary conversion in the Linux terminal. Using Python, you'll convert IP addresses between dotted-decimal and binary, translate CIDR masks, identify network/host portions, and calculate usable hosts and subnets for a given CIDR block.

This project will guide you through the process of creating a snake game using Python and the Pygame library. The game will have a game window, a snake, a power-up, and a score. The snake will move around the game window and eat the power-up. When the snake eats the power-up, the length of the snake will be increased by one. The score will be displayed on the screen.

In this project, you will learn how to create a Linux system monitor using Python and the Tkinter library. The system monitor will display real-time information about the CPU, RAM, disk usage, and network statistics.

In this project, you will learn how to build a network scanner using Python. The network scanner will utilize the nmap command-line tool to scan a specified IP address and display the results in a graphical user interface (GUI) using the Tkinter library.

In this lab, you will gain hands-on experience working with sets in Python. Sets are powerful data structures for storing unique elements and performing efficient membership tests. You will learn how to create sets, add and remove elements, perform set operations, and use sets to remove duplicates from lists.

In this lab, you will embark on your journey into Python programming by creating and running your very first program. You will gain hands-on experience using the VS Code editor within the LabEx environment to write and execute Python scripts. Building upon this foundation, you will further practice writing Python programs, focusing on utilizing the print() function to display output. The lab will then guide you through identifying and understanding common Python errors, equipping you with the knowledge to recognize and troubleshoot issues. Finally, you will learn practical debugging techniques to effectively resolve errors and ensure your Python programs function correctly.

In this lab, you will gain hands-on experience working with strings in Python, a fundamental data type. You will begin by understanding string basics, including creation, immutability, and accessing individual characters using both positive and negative indexing. Building upon this foundation, you will learn how to extract substrings using slicing, utilize various string operators and escape characters for manipulation and special formatting, and explore different methods for formatting strings. Finally, you will delve into common string methods to perform operations like searching, replacing, and modifying string content.