Machine learning (ML) has become such a buzzword that it’s not always easy to understand what it refers to. There’s a good reason for that ubiquity, though. A well-designed machine-learning algorithm is an excellent solution to many common annoyances organizations face, especially repetitive high-volume tasks.
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However, machine learning only works if done correctly, which often requires you to understand web scraping. Here’s how machine learning works, why web scraping matters, and how the right support can make your ML algorithms significantly more effective.
What Is Machine Learning?
Machine learning is one of the most common methods used to develop artificial intelligence (AI). It’s a branch of computer science that focuses on building algorithms that can learn from large data sets. The process is based on how humans learn, with the algorithm looking for patterns in the data and “learning” how to copy them and produce new examples of the pattern.
Thousands of different ML programs are used in everything from customer service to medical care. While every algorithm has its strengths, they all have in common: their reliance on excellent data sets.
All machine learning algorithms rely on huge collections called data training sets. These training sets contain the information and patterns the algorithms should learn from and imitate. The better the training sets, the more accurate the algorithm’s results. There are four ways machine learning programs can learn from training sets:
- Unsupervised learning: An unsupervised learning algorithm is fed unlabeled data. For example, it might be given tens of thousands of images of people or millions of words of news stories. The goal of unsupervised learning is to let the program find patterns independently and generate new data without human guidance. The program could generate new pictures of people or new fictional news stories based on the patterns it observed, but the user wouldn’t be able to request specific types of people or stories.
- Supervised learning: The alternative is supervised learning. In this case, the program is trained using labeled data. For example, it might be given images of people labeled with descriptions of their appearance. As a result, the program would learn to connect elements of the picture with the words in their description. This leads to an algorithm that can accurately produce new examples of the patterns in its training set. A user could request a picture of a “man in a red shirt” and get something approximately right.
- Semi-supervised learning: A semi-supervised model combines a small amount of labeled data with a large, unlabeled data set. The labeled data acts as a seed, and the algorithm applies labels according to its best guess to the rest of the input. This is most often used to train programs specifically to label data.
- Reinforcement learning: This is the closest to human learning. Reinforcement learning involves letting the program make multiple attempts to accomplish a task based on input data. It’s then given feedback on which of its results were best and refines its behavior accordingly. The most intensive ML method, but it also leads to the best results in situations where there are multiple solutions.
The Benefits of Machine Learning
Machine learning has many benefits in the modern world, such as:
- Spotting trends invisible to humans: ML can help identify patterns humans would never notice. Machine learning algorithms can spot trends and make connections by comparing all the data in their training sets in seconds. Many organizations choose to use machine learning to look for trends in customer behavior and opinions to make better decisions in the future.
- Taking over boring tasks: Most people lose their focus after an hour or so of doing a simple task over and over. However, computers are designed to do exactly that. A well-trained algorithm can take over repetitive tasks, giving people more time to focus on more complicated issues.
- Reducing payroll costs: Putting algorithms in charge of repetitive tasks can also reduce costs for businesses. Using ML to train a computer to handle these issues is significantly cheaper than paying people and doesn’t force humans to spend hours each day just looking at checks.
Examples of machine learning in real life:
How does ML actually get used in real life? There are many potential applications, but some of the most common include:
- Data classification: One of the best use cases for machine learning is data classification. A machine learning algorithm is excellent at learning from structured examples and correctly classifying new input. For instance, medical professionals have been experimenting with using ML to teach computers to diagnose potential new skin cancers by analyzing pictures of cancerous and non-cancerous moles.
- Chatbots: Many organizations have begun using ML to train chatbots to handle basic customer support tasks like answering FAQs and issuing new passwords.
- News articles: Some news outlets are experimenting with using ML combined with human editors to generate simple news articles to fill space on slow news days.
- Facial recognition: Many organizations, including Facebook and Google, use ML algorithms to study human faces to identify users in pictures and support biometric logins.
- Cyber security: In the continuing arms race against hackers, some cyber security experts are employing ML to learn what malicious attacks look like so they can prevent them without targeting innocent users.
Essentially, machine learning can be employed for any task that requires both precision and repetition.
How Is Web Scraping Connected to Machine Learning?
Is web scraping machine learning? Not quite. However, since machine learning relies heavily on large data sets, web scraping is a powerful tool for developing machine learning support. For many ML programs, combining web scraping with machine learning is the best way to collect huge collections of data that can be sanitized and fed to the program as a training set.
For instance, you could scrape search results for certain terms to collect pictures to train an image recognition algorithm. You could also scrape news sites to teach a program how to write news stories, social media sites to teach them about casual language, or classic book repositories to teach high-quality English. Regardless of what kind of data you need for your training sets, combining web scraping with machine learning is one of the best ways to collect information without wasting hundreds of hours doing it yourself.
Challenges of Web Scraping for Machine Learning
ML does present some complications. If teaching computers to teach themselves was easy, it would be used for everything. There are a few common challenges applying web scraping to machine learning that must be overcome to make it work for you. These include:
- Lack of high-quality data: The most critical feature of a successful machine learning project is a high-quality training set. If you don’t have a large enough data set, you won’t be able to train your program accurately. Similarly, if your data set is large but low-quality, you’ll train your algorithm to produce similarly low-quality results.
- Irrelevant data: Poor data collection can harm your ML project by including irrelevant data. If you want to train a program to recognize individual people, it shouldn’t be given pictures of pets, plants, or crowds, for instance. This irrelevant data will only confuse the final results.
- Data overfitting: If your data set isn’t broad enough, it may overgeneralize. If you want ML to generate realistic conversations, you must give it a wide range of examples of how people greet each other. Otherwise, it may repeat the same few simple statements and nothing else, no matter what people say.
- Data underfitting: On the other hand, a data set that’s too broad won’t teach your program anything useful. You can’t feed an algorithm every picture online and expect it to learn useful patterns. You need to limit the data set to just pictures of the subjects you want it to recognize, or it won’t be able to draw valuable conclusions.
Web scraping machine learning projects can help you resolve these issues. With a high-quality web scraper, you can collect the data you need in large quantities without gathering irrelevant information.
You don’t have to create a web scraper from scratch to kickstart machine learning web scraping projects. Instead, you can work with Scraping Robot to gather the data you need. Scraping Robot is a powerful tool that you can use to scrape a wide variety of data without having to code anything yourself.
What makes Scraping Robot such an excellent choice for machine learning web scraping?
- Cost-effective: Scraping Robot doesn’t charge hidden fees or monthly subscriptions. You simply pay for the number of scrapes you need. If you’re not sure how many you need, you can talk with the experts at Scraping Robot to determine which option works best for your project.
- Simplicity: With Scraping Robot, you don’t need to worry about having your IP address blocked, solving CAPTCHAs, managing proxies, or scaling browsers. You just need to identify the data you need and go.
- Structured JSON metadata output: All Scraping Robot APIs provide structured JSON output of a parsed website’s metadata. That means your data sets come with labeling elements no matter what, making it easy to train your web scraping machine learning program.
You don’t have to wait to get started on your next machine learning web scraping project. You can start using machine learning and web scraping together with Scraping Robot. You no longer have to worry about all the headaches that come with scraping, like proxy management and rotation, server management, browser scalability, CAPTCHA solving, and looking out for new anti-scraping updates from target websites. In addition, they have a dedicated support system and 24/7 customer assistance! You can learn more about how Scraping Robot will help your next project or get started today by exploring Scraping Robot’s simple pricing structures.
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