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Today we have the necessary technology to apply statistical techniques and algorithms to large volumes of data and do so in real time. These models that have the ability to understand and respond automatically to information are known as machine learning.
What is Machine Learning?
In a very superficial way, we can define machine learning as a type of Artificial Intelligence that develops techniques that allow machines to make decisions. They learn by finding patterns in the data and predicting later situations.
It is a really broad field that is constantly evolving and we can differentiate between:
Supervised learning: These algorithms start from the basis of prior knowledge on which to base decisions. Some of the most important algorithms are:
• Linear regression
• Logistic Regression
• Classification of Naïve Bayes
• Neural Networks
• SVMs (Support Vector Machines)
Non-supervised learning: In the non-supervised on the contrary, we do not have prior knowledge about the data and we try to find a structure or pattern. Some of the most important algorithms are:
• PCA (Principal component analysis)
• Clustering algorithms
•Principal component analysis
• Independent Component Analysis
Machine Learning Applications
We can talk about as many applications and situations as we want to imagine, there are sectors where it can be key for decision making such as medicine, where to decide whether to perform an operation or not to a patient or in the business world to know on which dates it is better to lower prices or estimate sales.
More applications… search engines, speech recognition, language or robotics. More examples that we see or will see in our day to day life may be:
The Machine Learning world covers a large field within the Big Data universe and is increasingly relevant. The number of techniques and algorithms is very extensive and continues to evolve. The applications are unimaginable.
However, it is not a future issue, the introduction of these techniques of Machine Learning in the phase of strategic decision making is practically an obligation today.