Machine Learning: Everything You Need to Know

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This topic, which is currently trending, is one of the favorites of large technology companies. And it couldn't be any different, after all, we are talking about an evolution that has proven to be very useful: Machine Learning.

Many people confuse Machine Learning with Artificial Intelligence. However, Machine Learning is just one area of ​​AI. Artificial Intelligence is a kind of umbrella concept that encompasses other areas of science and involves the expansion of computational capacity beyond data-driven learning.

But since the topic is so appealing and at the same time raises many questions, this post is dedicated to clarifying the concept of this technology and its applications a little more. Keep reading!

What is Machine Learning?
First, the theory: Machine Learning is the automation of analytical bangladesh telegram lead models so that they can develop new models without new programming. This allows machines to learn from their mistakes and know that they cannot use that algorithm again. In this way, they do not create models from them, but from the correct ones.

Still hard to understand? Let's try to explain it better. The internet stores information from billions of users around the world. A machine can take all this data, cross-reference it and extract insights. Then it cross-references the data again and feeds these insights into the algorithm, thus improving the algorithm and creating a model.

Every algorithm needs a task and a performance metric. Algorithms interact with new data and adapt until they reach the task that was set. All of this happens in a fraction of a second.

After “learning”, the machine can still share this information with others that are on the network, reducing the chances of error and making information processing much faster.

What types of Machine Learning exist?
There are four types of learning. However, two of them—supervised and unsupervised learning—are the most common. So let’s dive a little deeper into them and how they work.

Supervised Learning
Supervised learning is when the algorithm uses pre-established examples to indicate classifications or patterns. Here, some algorithms are used, such as decision trees, Naïve Bayes classification, logistic regression, among others.

From this guideline, the data is given labels of what it wants to find in the corresponding input and output. It then compares the input and output data and, if one does not match what was programmed, it reports the error.

This error is transformed into learning and is the basis for creating a pattern, which will now compare unlabeled data, but which has a historical record. An example of this is the classification of what will be sent to your spam box or not. You can even mark it, but it also learns that some words in email subjects are common spam.