You have already seen that machine learning is a method of pattern recognition. Machine learning reaches an answer by recognizing and sorting out patterns from the given learning data. It may seem easy when you just look at the sentence, but the fact is that it takes quite a long time for machine learning to sort out unknown data, in other words, to build the appropriate model. Why is that? Is it that difficult to just sort out? Does it even bother to have a "learning" phase in between?

The answer is, of course, yes. It is extremely difficult to sort out data appropriately. The more complicated a problem becomes, the more it becomes impossible to perfectly classify data. This is because there are almost infinite patterns of categorization when you simply say "pattern classifier." Let's look at a very simple example in the following graph:

There are two types of data, circles and triangles, and the unknown data, the square...

In the previous section, we saw that there could be millions of boundaries even for a simple classification problem, but it is difficult to say which one of them is the most appropriate. This is because, even if we could properly sort out patterns in the known data, it doesn't mean that unknown data can also be classified in the same pattern. However, you can increase the percentage of correct pattern categorization. Each method of machine learning sets a standard to perform a better pattern classifier and decides the most possible boundary—the decision boundary—to increase the percentage. These standards are, of course, greatly varied in each method. In this section, we'll see what all the approaches we can take are.

First, machine learning can be broadly classified into supervised learning and unsupervised learning. The difference between these two categories is the dataset for machine learning is labeled data or unlabeled data...

We have looked at the methods that machine learning has and how these methods recognize patterns. In this section, we'll see which flow is taken, or has to be taken, by data mining using machine learning. A decision boundary is set based on the model parameters in each of the machine learning methods, but we can't say that adjusting the model parameters is the only thing we have to care about. There is another troublesome problem, and it is actually the weakest point of machine learning: feature engineering. Deciding which features are to be created from raw data, that is, the analysis subject, is a necessary step in making an appropriate classifier. And doing this, which is the same as adjusting the model parameters, also requires a massive amount of trial and error. In some cases, feature engineering requires far more effort than deciding a parameter.

Thus, when we simply say "machine learning," there are certain tasks that need to...

In the previous section, you saw the general flow of when we perform data analysis with machine learning. In this section, theories and algorithms of neural networks, one of the methods of machine learning, are introduced as a preparation toward deep learning.

Although we simply say "neural networks", their history is long. The first published algorithm of neural networks was called perceptron, and the paper released in 1957 by Frank Rosenblatt was named The Perceptron: A Perceiving and Recognizing Automaton (Project Para). From then on, many methods were researched, developed, and released, and now neural networks are one of the elements of deep learning. Although we simply say "neural networks," there are various types and we'll look at the representative methods in order now.