How To Learn Machine Learning And Turn Your Company

How to learn Machine Learning and turn your company

 One of the most ambitious ideas of artificial intelligence (AI) is Machine Learning (ML), in Spanish machine learning, which creates methods that automatically learn, through specific algorithms.

He maintains that systems can learn from data, recognize patterns and make decisions with minimal human intervention.

But the Machine Learning goes further: it focuses on doing statistical research and recovering information in engineering, mathematics, computer and other fields.

Its main objective is to address and solve practical problems where any of the numerical disciplines are applied.

What is Machine Learning?

In 1959, Arthur Samuel defined her as a field of computer science that gives teams the ability to learn without being explicitly scheduled.

It allows many operations to be carried out by reducing the need for human intervention.

As a discipline you can break down at: the Shallow Learning and the Deep Learning. Both subdisciplins have in turn with supervised and non -supervised instructional systems.

Another important thing is that with ML you can do complex things. It is present today in many applications such as Spotify or Netflix, as well as in Gmail’s smart answers.

There are many more, but these are some of the most common today:

• Customer Support.
• Patterns detection.
• Identification of errors/fraud in financial operations.

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• Detect intrusions in a data communications network.
• Prediction of incidents in technological environments
• Forecast of temporary business conditions
• Business Security.
• Medical diagnoses.
• Urban mobility
• Know what is the best time to publish tweets, Facebook updates or send the newsletter.

Differences between Machine Learning and Deep Learning

The Deep Learning combines advances in computation and special types of neural networks to learn complicated models in large amounts of data.

It is a more sophisticated type of Machine Learning algorithm, built from the principle of neural networks.

While in the ML, the algorithms can be provided with data and learn on their own to make predictions, in the DL you work with Big Data and functions as its own mind through nonlinear layers of data processing.

Types of Machine Learning

This is based on evidence and experiences in the form of data. According to the number of examples that occur in a situation, a model is prepared that deduces and generalizes a behavior already seen; From this employer proceeds to make predictions for new cases. Here the types of Machine Learning:

Supervised learning

The human provides data, with their respective labels, to the system for this one training. The idea is that PC learn from examples in recognition of voice and writing, spam detection, among others. Example: Distinguish ball car.

Not supervised learning

It is a training method similar to the way in which information processes a human being, but without labels, on the contrary, a large amount of data is provided with the characteristics of an object (car and ball elements)

Reinforcement learning

From experience, systems learn based on tests and errors, optimizing their behavior. That is, the algorithm progressively associates the success patterns, to repeat them until they are perfected and becoming infallible.

Examples: navigation of a vehicle in automatic, decision making, etc.

Machine Learning applied to cybersecurity

The ML has revolutionized the business and social world thanks to the applications and can be taken advantage of in the area of ​​cybersecurity, especially since cyber -cybermen are not always stable. They evolve and arise more sophisticated.

There are those who say that Deep Learning subdiscipline is less effective than shallow learning in the detection of cyber threats.

In this sense, Machine Learning is an ideal tool to combat them, given their adaptation and learning capabilities.

In general, Machine Learning algorithms are Deep or Shallow, supervised or non -supervised, are more capable of recognizing cyber attacks if they focus on a single threat, instead of several at the same time.

What helps the ML in companies?

ML -based technologies will increasingly help to combat fraud, evaluate and optimize business processes, improve test procedures and develop new solutions to existing problems.

Detects intruders due to its ability to analyze behavior patterns to entering systems.

Protects sensitive equipment such as commercial point of sale (TPV) terminals, which use user bank information.

Minimizes vulnerabilities: especially when cybersecurity responsible do not have time to defend systems and have to focus on alleviating the damage caused.

When abomalities or suspicious situations are detected on connected devices.

Machine Learning is that, like cybersecurity, it advances very dynamically and quickly.

The potential capacity of intelligent learning of machines to improve themselves makes this technology a powerful weapon in computer security to take into account.

conclusion

Machine Learning has evolved quite a lot since its creation and today, thanks to it you can create behavior models to analyze large volume and complexity data.

In addition, systems provide rapid and precise results without human intervention, even on a large scale.

The result: high value predictions to make better decisions and develop better business actions.

The practical field of application depends on the imagination and data available in the company.