In this big data and digital transformation era, where the data is everywhere, we need to adopt AI technologies to find patterns, correlations and valuable insights in our data.

Machine learning it’s a branch of  artificial intelligence where this technology can learn from data, identify patterns and make decisions with minimal and, in some cases, to zero human intervention.

Machine learning is based on mathematical and statisticals models and the basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.

Machine learning performs through mathematical and statistical models

The main types of Machine learning categories are divided in:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Which one use?

It depends on the business needs.

A Machine learning algorithm is going to be useful as long as we identify correctly the business question that need to be answered. In this case, you require a data scientists with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training.

Supervised learning

In supervised learning for example, data scientists determine which variables or features the model should analyze and use to develop predictions. Once the training phase is complete, the algorithm is ready to apply what was learned in the previous data with new data.

Unsupervised learning

In the case of unsupervised learning is used with data that has no historical labels. Here the interest thing is that the algorithm is not telling you the “right answer.”

The algorithm must figure out what is being shown. The objective of this kind of methodology is to explore the data and find some structure within.

Reinforcement learning

Reinforcement learning, this is often used for robotics and gaming .

With reinforcement learning, the algorithm discovers through trial and error which actions are the best. The algorithms works with a “0″ like punishment and “1” for a reward.

This type of learning algorithms has three main components:

  • The agent (the learner)
  • The environment (where the actions occur)
  • The actions (the activity of the agent)

In dataismm.ai we’re experts in apply machine learning techniques to real business problems.

Let us guide you apply this amazing technology to answer your business questions and run predictive models in benefit of your business.

Remember, we’re experts in:

Converting your data in the power of decision

Contact us now!

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