Machine Learning: What is it, concept and definition

 


Machine Learning (ML) is an important area of artificial intelligence where it is possible to create algorithms to teach a given machine to perform tasks. An ML algorithm makes it possible to take a set of input data and based on certain patterns found, generate the outputs. Each entry in this dataset has its own features, and having a set of them is the fundamental starting point for any ML algorithm.

DEFINITION OF MACHINE LEARNING

In general, Machine Learning is a field of artificial intelligence that aims to explore studies and constructions of algorithms that make it possible to understand autonomously. It is possible with ML to recognize and extract patterns from a large volume of data, thus building a learning model. This learning is based on observation of data such as examples, direct experience, or instruction. Once they have learned, they are able to perform complex and dynamic tasks, predict more accurately, react in different situations and behave intelligently.

We can define ML, therefore, as a kind of field of study that allows computers to learn the skill without being necessarily programmed. In other words, it is a way of doing better in the future based on past experiences.

MACHINE LEARNING: MORE AND MORE IN EVIDENCE

Interest in machine learning has recently resurfaced because of the same factors that have made data mining and Bayesian analysis more popular than ever before. The growing volume and variety of data available, the computational processing that is cheaper and more powerful, and the storage of data in an accessible way are also responsible for the growth of this type of process.

All of this means that you can quickly and automatically produce models that allow you to analyze larger and much more complex data, delivering faster and more accurate results – even on a very large scale.

The result? High-value predictions can lead to better decisions and smart actions in real-time without human intervention. That is much more accurate and a much more qualified and assertive investment.

 How does machine learning interfere these days?

Maybe you've wondered, but never really gotten to the bottom of the answer to how an online retailer presents almost instant offers for other products that might interest you. Or even noticed how lenders can provide near real-time responses to your loan requests?

Many of our simplest daily activities are powered by machine learning algorithms that can include:

  • Fraud detection.
  • Web search results.
  • Real-time ads, both on web pages and on mobile devices.
  • Text-based sentiment analysis.
  • Credit score and best deals.
  • Equipment failure prediction.
  • New pricing models.
  • Intrusion detection on a given network.
  • Recognition of certain patterns and images.
  • Email spam filtering.

What are the most popular methods?

There are two commonly used learning methods. Are they:

  • Supervised learning: which consists of labeled examples. The learning algorithm takes a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing the actual output with the correct outputs to find errors. It then modifies the model accordingly.
  • Unsupervised Learning: Basically it is used against data that does not have historical labels. That is, the system does not know the “right answer” in this case. The algorithm must discover what is being shown and the objective is to explore the data and thus find some structure in it. Unsupervised learning works well on transactional data.
  • semi-supervised learning: It is generally used for the same applications as supervised learning, but it can use both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (since unlabeled data are cheaper and require less effort to acquire). This type of learning can be used with methods such as classification, regression, and prediction. Semi-supervised learning is very useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a webcam.
  • Reinforcement learning: This option is often used for robotics, games, and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions generate the best rewards.

Therefore, Machine Learning or Machine Learning is a data analysis method that automates the development of analytical models, through algorithms that interactively learn from data, thus allowing computers to find hidden insights without being explicitly programmed to look for them. something specific!

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