Structured vs Unstructured Data:
Structured: data that can be represented by a two-dimensional table, stored in a database
Unstructured data: including all formats of office documents, text, pictures, XML, HTML, various reports, images and audio/video information, etc.
Structuring: in the middle
Machine learning can be divided into structured data (tables), semi-structured data (text, logs, etc.) and unstructured data (pictures, videos) according to the input data. In fact, the semi-structured data is unstructured by extracting features. Convert the data into structured data, and then perform machine learning.
Supervised and Unsupervised Learning:
Transfer: https://blog.csdn.net/jwh_bupt/article/details/7654120
The common methods of machine learning are mainly divided into supervised learning and unsupervised learning . Supervised learning, which is often referred to as classification, is trained through existing training samples (that is, known data and its corresponding output) to obtain an optimal model (this model belongs to a set of functions, and the optimal means in a certain function. It is the best under the evaluation criteria), and then use this model to map all the inputs to the corresponding outputs, and make simple judgments on the outputs to achieve the purpose of classification, which also has the ability to classify unknown data. In people's understanding of things, we have been taught by adults that this is a bird, that is a pig, that is a house, and so on. The scenery we see is the input data, and the results of adults' judgment on these scenery (whether it is a house or a bird) are the corresponding output. When we have more knowledge, we gradually get some generalized models in our minds, which are the (or those) functions obtained by training, so that we can distinguish which ones without the need for adults to point out. which are houses and which are birds. Typical examples in supervised learning are KNN and SVM. Unsupervised learning (some people call it unsupervised learning, it's almost the same anyway) is another learning method that has been studied more. It is different from supervised learning in that we do not have any training samples in advance, but need to directly analyze the data. model. It may sound weird, but unsupervised learning is used in many places in our own understanding of the world. For example, when we visit an art exhibition, we know absolutely nothing about art, but after appreciating multiple works, we can also divide them into different factions (such as which are more hazy and which are more realistic, even if we do not know what It is called hazy school, what is called realism school, but at least we can divide them into two categories). A typical example of unsupervised learning is clustering . The purpose of clustering is to group similar things together, and we don't care what the class is. Therefore, a clustering algorithm usually only needs to know how to calculate the similarity to start working.
Transfer: https://blog.csdn.net/u010681011/article/details/50347237