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A Beginners Guide to Understand 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 is given raw data, and the computer makes calculations based on it. The difference between traditional computer systems and machine learning is that with traditional systems, a developer has not incorporated high-level code that would differentiate between things. Therefore, it cannot make perfect or refined calculations. But in a machine learning model, it is a very refined system incorporated with high-level data to do extreme calculations at the level that matches human intelligence, so it is able to make extraordinary predictions. It can be broadly divided into two specific categories: supervised and unsupervised. There is another category of artificial intelligence called semi-supervised.
With this type, a computer is taught what to do and how to do it with the help of examples. Here, a computer is given a large amount of labeled and structured data. A drawback of this system is that a computer requires a large amount of data to become an expert at a given task. Data that serve as inputs enter the system through various algorithms. Once the procedure of exposing computer systems to this data and mastering a given task is completed, you can provide new data for a new and refined response. Different types of algorithms used in this type of machine learning include logistic regression, K nearest neighbors, polynomial regression, naive bins, random forest, etc.
With this type, the data used as input is not labeled or structured. This means that no one has seen the data before. This also means that the input can never be guided into the algorithm. The data is only fed into the machine learning system and used to train the model. It tries to find a particular pattern and provide an 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 tools, etc.
ML reinforcement 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 most effective with the most efficient results. There are mainly three components involved in machine learning: the agent, the environment, and the actions. The agent is the learner or decision maker. The environment is the atmosphere with which the agent interacts, and actions are considered the work that an agent does. This happens when the agent chooses the most effective method and proceeds based on it.
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