Machine Learning Write For Us – Machine learning is a branch within the field of Artificial Intelligence that provides systems with the aptitude to learn and improve mechanically based on experience. These systems transform data into info, and with this information, they can make decisions. For a model to make robust predictions, it must be fed with data. The more, the better. Fortunately, today the web is full of data sources. On many occasions, the data is collected by private companies for their own benefit, but there are also other initiatives, such as open data portals.
Types of machine learning
Depending on the available data and the task we want to tackle, we can choose between different types of learning. These are supervised, unsupervised, semi-supervised, and reinforcement learning.
supervised learning
Suppose we have an ice cream parlor, and for the last few years, we have been recording daily weather data, temperature, month, day of the week, etc., and we have also done the same with the number of ice creams sold each day. In this case, we would surely be interested in training a model that, based on weather data, temperature, etc. (characteristics of the model) of a specific day, tells us how many ice creams are going to be sold (the label to predict). That is to say, Supervised learning requires labeled data sets, that is, we tell the model what we want it to learn.
Unsupervised learning
For its part, unsupervised learning works with data that has not been labeled. We don’t have a label to predict. These algorithms are mainly used in tasks where it is necessary to analyze the data to extract new knowledge or group entities by affinity.
This type of learning also has applications to reduce dimensionality or simplify data sets. As an example of unsupervised learning, we have grouping or clustering algorithms, which could be applied to find customers with similar characteristics to whom to offer certain products or allocate a marketing campaign, topic discovery or anomaly detection, among others. In the case of grouping data by affinity, the algorithm must define a similarity or distance metric that will be used to compare the data with each other.
Semi-supervised learning
Sometimes it is tough to have a wholly labeled data set. Let’s imagine that we are the owners of a dairy product manufacturing company, and we want to study the brand image of our company through the comments that users have posted on social networks. The impression is to create a model that classifies each word as positive, negative, or neutral and then carry out the study. That is to say, The first thing we do is dive through social networks and collect sixteen thousand messages that our company mentions. The problem now is that we don’t have a label in the data; that is, we don’t know the sentiment of each comment. This is where semi-supervised learning comes into play. This type of learning has a bit of the previous two. Using this approach, you start by manually tagging some of the comments.
reinforcement learning
Finally, reinforcement learning is a machine learning method that rewards desired behaviors and penalizes unwanted ones. By applying this method, an agent is capable of perceiving and interpreting the environment, executing actions, and learning through trial and error. Knowing long-term goals for maximum overall reward and optimal solution. Play is one of the most lengthily used arenas for testing reinforcement learning. AlphaGo or PacmanThere are some games where this technique is applied. In these cases, the agent receives information about the game’s rules and learns to play by himself. At first, obviously, he behaves randomly, but over time he begins to learn more sophisticated moves. This type of learning is also applied in other areas, such as robotics, resource optimization, or control systems.
supervised learning
First, the type of supervised learning is based on surveillance. A project with this technique must train the machines with a perfectly labeled database, which leads it to predict specific output data. In simple terms, with supervised learning, we tell the machine what we want to learn, and it must follow it to the letter. That is to say
For example, we can relate the Keep Coding boot camps and at what time of the year more people sign up for one of them. Thus, we would train a model that manages to decipher the relationship between the time of year and the number of enrollees.
Unsupervised learning
As its name reveals, this type of machine learning differs from the previous one in that it is not supervised at any time. In this case, the machine will act on its own and will not need labels, but a group of data, to give results.
Thus, the machine should try to integrate or group all the results in labels by relation. Meanwhile, the algorithm must find a way to perform measurements and the relationships between all the results found.
semi-supervised learning
Semi-supervised learning is a practice that is in the middle between supervised and unsupervised learning. This way, only a minimal set of tags is used. However, most unlabeled data sets, as they increase costs, are helpful in meeting the objectives.
enforcement learning
The last type we will discuss is the reinforcement learning type, a practice based on rewarding desired behaviors, while the unwanted ones will penalize. It is a process based on feedback since the machine will learn from experiences, development, and performance.
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