Brief introduction to Machine Learning
Currently developing or acquiring transactional information systems should not be the last and only goal of technology departments. On the contrary, one of the main purposes should be to take advantage of all that data that has accumulated over the years and use it to add real value to the business and generate an advantage competitive in the market. At this point is where Machine Learning contributes its grain of sand.
But what is exactly Machine Learning, well Arthur Samuel define ML as:
Field of study that allows computers to learn without being explicitly programmed.
Although Machine Learning is not a new concept, in recent years it has had an exponential boom in its use and application. One of the main reasons for this boom is the increase in processing capacity and the decrease in its costs, thus allowing it to be available to everyone.
Within Machine Learning, the algorithms used can be grouped into two types:
Supervised learning: Supervised learning is one that for a set of input data we know in advance the correct output data.
Supervised learning can be separated into two classes of algorithms:
- Classification uses an algorithm to accurately assign test data into specific categories.
- Regression is used to understand the relationship between dependent and independent variables
Unsupervised learning: In unsupervised learning for a set of input data, we do NOT know the output data in advance. It is used to obtain a coherent grouping of the data based on the relationships between the variables defined in the data.
Unsupervised learning classified into two categories of algorithms:
· Clustering: Is where objects are grouped in regions where mutual similarity is high.
· Association: Is where you want to discover rules that describe large portions of your data.
ML application
Many activities are currently taking advantage of Machine Learning. Sectors such as online shopping, online advertising or finance have been taking advantage of these technologies for some time.
The practical field of application depends on the imagination and the data that are available in the company. The technology is there. The data too. Why wait to try something that can open the door to new ways of making data-driven decisions?
