A Beginner’s Guide to Understanding Machine Learning

What is machine learning?

Machine learning is a branch of artificial intelligence that involves a computer and its calculations. In machine learning, the computer system receives raw data and the computer performs calculations based on it. The difference between traditional computer systems and machine learning is that with traditional systems, a developer hasn’t built in high-level code that would make distinctions between things. Therefore, you cannot make perfect or refined calculations. But in a machine learning model, it’s a highly refined system that incorporates high-level data to do extreme calculations at the level that matches human intelligence, so it’s capable of extraordinary predictions. It can be broadly divided into two specific categories: supervised and unsupervised. There is also another category of artificial intelligence called semi-supervised.

LA supervised

With this type, a computer is taught what to do and how to do it with the help of examples. Here, a computer receives a large amount of labeled and structured data. One drawback of this system is that a computer requires a large amount of data to become an expert on a particular task. The data that serves as input enters the system through various algorithms. Once the procedure of exposing computer systems to this data is complete and mastering a particular task, you can provide new data for a new and refined response. The different types of algorithms used in this type of machine learning include logistic regression, K-nearest neighbors, polynomial regression, naive bayes, random forest, etc.

Unsupervised machine learning

With this type, the data used as input is not labeled or structured. This means that no one has looked at the data before. This also means that the input can never be guided to the algorithm. The data is only fed into the machine learning system and used to train the model. Try to find a particular pattern and give the answer that is desired. The only difference is that the work is done by a machine and not by a human being. Some of the algorithms used in this unsupervised machine learning are singular value decomposition, hierarchical clustering, partial least squares, principal component analysis, fuzzy means, etc.

reinforcement learning

Reinforcement ML is very similar to traditional systems. Here, the machine uses the algorithm to find data through a method called trial and error. After that, the system itself decides which method will be the most effective with the most efficient results. There are mainly three components included in machine learning: the agent, the environment, and the actions. The agent is the one who learns or makes decisions. The environment is the atmosphere with which the agent interacts, and the actions are considered the work that an agent performs. This occurs when the agent chooses the most effective method and proceeds based on that.

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