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This article mainly introduces what problems can be solved by machine learning in the computer network, which can be used for reference by interested friends. I hope you will gain a lot after reading this article. Let's take a look at it.

Machine learning is a special study of how computers simulate or realize human learning behavior in order to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance. Machine learning can solve the following problems: 1, classification problem; 2, regression problem; 3, clustering problem.

The operating environment of this tutorial: windows7 system, Dell G3 computer.

What is machine learning?

Machine learning is to learn part of the data of the computer, and then predict and judge some other data.

The core of machine learning is to use algorithms to parse data, learn from it, and then make decisions or predictions about new data. In other words, a computer uses the data obtained to obtain a model, and then uses this model to predict, this process is somewhat similar to the human learning process, such as people gain certain experience, can predict new problems.

Let's take an example. We all know about Alipay's Spring Festival activity of "collecting five blessings". We use mobile phones to scan photos of "Fu" to identify Fu characters. This is the method of machine learning. We can provide the computer with photo data of the word "Fu". Through the training of the model of the algorithm, the system will constantly update and learn, and then enter a new photo of the word "Fu", and the machine will automatically identify whether there is a word "Fu" on this photo.

Machine learning is a multi-domain cross-discipline, which involves many disciplines, such as probability theory, statistics, computer science and so on. The concept of machine learning is to train the model by inputting a large amount of training data to make the model master the potential rules contained in the data, and then accurately classify or predict the newly input data. As shown in the following figure:

What kind of problems can machine learning solve?

From a functional point of view, machine learning can solve the following problems on a certain order of magnitude of data:

1. Classification problem: according to the characteristics extracted from the data sample, determine which of the limited categories it belongs to. For example: spam identification (result category: 1, spam 2, normal mail).

2. Regression problem: predict the result of a continuous value according to the characteristics extracted from the data sample. For example, Sing Yeh's Mermaid at the box office

3. Clustering problem: according to the characteristics extracted from the data samples, let the samples hug the group (the similar / related samples are in a group). For example: google's news category.

Let's classify the above common problems into the two most typical categories of machine learning.

Classification and regression problems need to be trained with data of known results, which belongs to "supervised learning".

The problem of clustering does not need a known label and belongs to "unsupervised learning".

If you stroll around the IT industry (especially the Internet), you will find that machine learning is widely used in the following hot issues:

Thank you for reading this article carefully. I hope the article "what problems can be solved by machine learning in computer network" shared by the editor will be helpful to everyone? at the same time, I also hope that you will support and pay attention to the industry information channel. more related knowledge is waiting for you to learn!

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