Every day the use of machine learning is becoming an indispensable way for companies in various segments to analyze data in a more accurate and automated way.
Machine learning is increasingly popular thanks to the digital transformations of companies that are able to identify more profitable opportunities and avoid dangerous mistakes with this technique that allows them to deliver much more accurate and faster results.
In this article we will explain a little about what Machine Learning is and how it is used in the market.
What is Machine Learning?
The Machine learning technique is a specific branch of AI that trains machine to learn from data. These machines have the ability to learn on their own from large volumes of data through big data algorithms. These machines identify patterns in data and create intelligent connections, learning and performing tasks without human help.
The goal of this process is to predict responses more accurately and deliver the best result with the least chance of error.
This technology can be separated into two main categories: supervised or unsupervised.
Supervised: In this category, the interaction of a human being is required to control the output and input of data and if needed, interfere in the machine training by making comments for learning and application in the next analyses of the machine in question.
Unsupervised: In this category, machine learning is used against data that has no historical labels. They use Deep learning to process complex tasks without human training.
What are the advantages of using Learning Machine?
- By delivering unlimited data from diverse sources, it can constantly review data and help message based on behavior. Once trained, the machine can identify more relevant variables and convey the right information. In addition, it is an effective way to automate internal company processes.
- It can quickly process, analyze, and predict customer needs, promoting a more customized experience for each moment or individual. This is because learning happens through past behavior, thus improving predictions based on new data.
- It can be used to identify customer segmentations, or create micro segmentation based on behavioral patterns. This type of learning helps create a preemptive approach to customer segmentation, thus guiding each individual customer through the buying journey.
Is there a difference between artificial intelligence, machine learning, and leed learning?
Despite working with machine learning and data analysis, these three parts have their own characteristics and ways of working and delivering results.
Artificial intelligence, for example, has the ability to mimic some human characteristics through visual perception and speech recognition. Everything it learns is based on or inspired by human characteristics, or human behaviors.
The technique of Machine Learning technique, learning happens without actually being programmed, the machine adjusts itself to give answers according to the data it already has available for analysis. In this technique, human intervention is not in fact necessary to make the changes, since the objective here is a dynamic learning through already existing data.
With more complexity than the Machine Learning technique, the Deep Learning subset is mainly used to refer to the more complex artificial neural networks. Using sets of algorithms that establish new, more accurate records for various problems with actions such as image and sound recognition, systems and recommendation, among many others.
Examples of application of Machine Learning today
- Autonomous cars from Google (Essence)
- Recommended offers such as those from Amazon and Netflix (Day-to-day)
- Knowing what customers say about you on twitter (Linguistic rules)
- Fraud detection
It is not hard to see companies using the technique. Currently sectors like financial services already use Machine Learning to identify fraud or investment opportunities.
Marketplace and online stores use it mainly to improve customer experience and fulfill wants and needs through product recommendations based on previous purchases, or analyze purchase history and searches to promote items of interest thus personalizing the experience on the site.
Now that you know a bit about this technique, how about further customizing your customer's experience and making the shopping experience more secure? This is a trend for the coming years and many businesses are already adapting to this new phase.
Did you like today's article? Are you familiar with this technique? Come and tell us about it.
