Artificial intelligence and machine learning are technologies that can automate routine machine tasks so that an organization’s talent can focus on adding value to the business. However, these technologies and real-world examples are still not well-known to many enterprises, especially small and medium-sized enterprises, who have not dared to make the decisive leap forward in digital transformation.
The first thing you need to do to enter the world of machine learning is to know that there are three types of machine learning: Supervised, unsupervised learning and reinforcement learning.
Supervised learning describes a class of problems that involves the use of a model, an algorithm, so that the program can learn from experience.
These models are fitted to training data composed of both input data and output data. These are used to make predictions on test sets. For this, only the input data is provided while the output data of the training model is compared with the target variables.
Supervised machine learning, on the other hand, is about providing data labeling algorithms to guide you through the process. Once the model is well trained to provide sufficiently representable data, it can be deployed to production.
Unsupervised Machine Learning
Compared to supervised learning, unsupervised learning operates only on the input data with no outputs or target variables. Furthermore, as such, unsupervised learning does not have a person who is dedicated to correcting the input model to facilitate learning by the program or machine.
You can organize different data points into different groups depending on which attributes are important. For example, mix the fruits and vegetables. After you provide this combination of fruit and vegetable data to the model, in this case, the model picks colors as a default feature and groups them accordingly.
2 Main Type Of Unsupervised Machine Learning
Unsupervised learning is a very useful type of learning for anomaly detection because it looks for patterns within unlabeled data. When an algorithm continuously receives similar data, it can easily identify patterns or data inputs that do not share similar behavior or properties with the rest of the data. In time series data, this is usually applied to predictive maintenance.
Recommendation systems are widely implemented in various industries. Clustering is essentially the main type of unsupervised machine learning used in these systems. E-commerce stores (eBay or Amazon), entertainment platforms (YouTube or Netflix), social networks (Facebook or LinkedIn), etc. all use recommendation systems to find patterns and similarities, products to buy, videos, movies and series to watch, or friends to add.
As in the example below, Netflix’s recommendation engine recommends ‘shows to watch’ to users because they share similar attributes of the show they’ve just watched.
It is similar to supervised learning in that the model has some feedback to learn from, although the feedback may be delayed, making it difficult for the machine to interpret the training model and cause and effect well.
An example of reinforcement learning is a game in which the machine has the objective of obtaining a high score and, to achieve this, it can make movements that, depending on whether they are correct or incorrect, end up receiving reinforcement or punishment comments.
It is a type of machine learning that transfers classical conditioning to machine learning.
What Algorithms Exist In Machine Learning?
For machine learning to work, for machines and programs to be able to learn with any of the types of Machine Learning, a series of algorithms are necessary:
- Regression:With them, the machine learning program makes estimates and understands the relationships between the variables to be studied.
- Bayesians:These algorithms classify the values independently of any other data in the set that you want to study. It is one of the most implemented since it allows highly complex data classifications.
- Grouping:They are used in unsupervised machine learning since they allow organizing and categorizing all the data that is not labeled.
- Decision tree. It is a very useful tool for the machine learning program to decide based on a series of pre-established criteria. It looks like a flowchart.
- Neural networks: Used in Deep Learning, these are artificial neural networks that base their operation on that of the human brain. It establishes connections between the nodes (neurons) in which they connect to each other to solve the problems posed.
- Deep learning:These are all those that are capable of executing data through composite layers through neural networks in which they pass a simplified representation of the data.