Machine Learning - Traditional AI

When computing power was a fraction of what it is today, the journey of AI began in the 1950s. AI began with the machine making predictions similar to how a statistician uses a calculator to make predictions. Thus, the entire AI development was mainly based on statistical techniques.

Let's examine these statistical techniques in detail in this chapter.

Statistical Techniques

In school, you probably used straight-line interpolation to predict a future value. This is how AI applications were developed. In the development of so-called artificial intelligence programs, several other statistical techniques are successfully applied. In spite of the fact that today's AI programs are much more complex and use techniques far beyond the statistical techniques used in the early AI programs, we call them "so-called" because they are far more sophisticated.

The following are some examples of statistical techniques that were used for developing AI applications in those days and are still in use today.

• Regression

• Classification

• Clustering

• Probability Theories

• Decision Trees

You can use these statistical techniques if you are developing AI applications based on limited data, although we have listed only a few primary methods here.

In the present day, data is abundant. To analyze the kind of huge data we possess, statistical techniques are insufficient due to their own limitations. To solve many complex problems, more advanced methods are developed, such as deep learning.

Throughout this tutorial, we'll learn what Machine Learning is and how it's used for developing such complex AI applications.

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