How to scrape data from website？

Artificial intelligence (AI) is a technology that enables computer systems to simulate human intelligence and perform tasks such as learning, reasoning, recognition, and decision-making. With the rapid development of AI, Python has become one of the most widely used programming languages in this field because of its simple syntax, ease of learning, rich ecosystem, and high development efficiency. This article introduces the main applications of Python in artificial intelligence from three aspects: machine learning, natural language processing, and computer vision. 1. Machine Learning Machine learning is an important branch of artificial intelligence. It focuses on enabling computers to learn patterns from data and perform tasks such as prediction, classification, and recognition without being explicitly programmed. Python has a rich collection of libraries and frameworks for machine learning. Commonly used libraries include NumPy, pandas, and scikit-learn. NumPy is mainly used for nume...

  Totaljobs is one of the largest recruitment websites in the UK. Its mission is to provide job seekers and employers with efficient recruitment solutions and promote the matching of talents and positions. It has an extensive market presence in the UK, providing a platform for professionals across a variety of industries and job types to find jobs and recruit staff. Introduction to the scraping tool ScrapeStorm is a new generation of Web Scraping Tool based on artificial intelligence technology. It is the first scraper to support both Windows, Mac and Linux operating systems. Preview of the scraped result Export to Excel: 1. Create a task (1) Copy the URL (2) Create a new smart mode task You can create a new scraping task directly on the software, or you can create a task by importing rules. How to create a smart mode task How to import and export scraping task Press enter or click to view image in full size 2. Configure the scraping rules Smart mode automatically detects the fie...

Machine learning continues to shape how businesses analyze data, automate decisions, and build intelligent products. For developers, researchers, and technology leaders, GitHub remains one of the best places to discover practical machine learning projects, study real implementations, and identify tools that can be adapted for production use. From deep learning frameworks to end-to-end application platforms, the open-source ecosystem offers a wide range of machine learning projects that reflect the latest progress in AI. In this article, we highlight five of the most influential and widely recognized machine learning projects on GitHub. These projects stand out because of their technical depth, strong communities, broad adoption, and real-world impact. 1. TensorFlow GitHub: https://github.com/tensorflow/tensorflow TensorFlow is one of the most widely used machine learning frameworks in the world. Developed by Google, it provides a comprehensive ecosystem for building, training, and dep...

Year after year, you can easily find articles claiming “PHP is dead”. Yet real-world data keeps telling a completely different story: Most websites across the globe still rely on PHP in 2026. According to W3Techs statistics, PHP powers roughly 70%+ of all websites with known server-side programming languages. The long-lasting dominance is not accidental. Let’s break down why PHP remains irreplaceable on the modern internet. 1. Unmatched CMS Ecosystem Forms Solid Moat The biggest driver behind PHP’s market share is WordPress, which alone runs around 43% of all websites worldwide. Millions of blogs, business sites, online stores and news portals are built on WordPress, Drupal, Joomla and Magento — all written in PHP. Migrating these existing platforms to Node.js, Python or Go requires huge time and capital investment. Most small businesses and web owners choose continuous maintenance rather than full reconstruction. This massive legacy installation guarantees steady PHP demand for years ...

ScrapeStorm is an AI-powered visual web scraping tool designed for no-code data collection, ideal for users without programming experience. Below is a brief overview of its strengths and weaknesses. Advantages Low learning curve, no coding required It features an AI Smart Mode that automatically detects lists, tables and paginated content after entering a URL. The visual flowchart mode allows users to set scraping rules simply by clicking, enabling quick adoption. Cross-platform support Compatible with Windows, Mac and Linux. Scraping tasks can be synced to the cloud for access on different devices. Multiple export options Captured data can be exported to Excel, CSV, TXT or directly saved into databases such as MySQL for further sorting and analysis. Comprehensive built-in features It supports scheduled scraping, basic data cleaning, image downloading and more, covering most common web data collection scenarios. Disadvantages Strict limitations on the free version Caps are applied to f...

 Fifteen years ago, extracting data from the web meant writing Python scripts, debugging XPath, handling anti-scraping mechanisms — a task that took an entire day. Today, open ScrapeStorm, enter a URL, click a few times, and the data is automatically exported to Excel in under five minutes. The craft of data extraction has undergone a quiet revolution in just two decades. Four Stages Phase One: The Age of Code Heroes. Scraping was a skill reserved for programmers. Mastery of programming languages, scraping frameworks, HTML, and regular expressions was required. The barrier was extremely high; ordinary users had to rely on technical teams or resort to manual copy-pasting. Phase Two: The Age of Client Tools. Visual point-and-click tools emerged, allowing users to scrape without writing code. However, these were essentially “rule generators” — you clicked on elements, and the tool generated XPath rules. When websites changed, the rules broke, requiring reconfiguration. Phase Three: Th...