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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 computers and their calculations. In machine learning, a computer 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, the developer has not included high-level code that would create differences between objects. Therefore, it cannot produce perfect or refined calculations. But in the example of machine learning, it is a very refined method that is combined with high-quality data to make high-level calculations at a level similar to human intelligence, so it can make amazing predictions. It can be broadly divided into two categories: supervised and unsupervised. There is another category of artificial intelligence called semi-supervised.
With this model, the computer is taught what to do and how to do it with the help of examples. Here, the computer is given written and processed information. One disadvantage of this system is that the computer requires a large amount of data to become proficient at a particular task. Data that serves as an input goes into the system through various processes. The process of showing the computer system to this data is to know well when a certain task is completed, you can provide new data for a new and refined solution. Different types of algorithms used in this type of machine learning include regression, K-nearest neighbors, polynomial regression, naive bayes, random forest, and more.
With this model, the data used as input is not typed or sorted. This means that no one has looked at the data before. This also means that the input cannot be directed to the algorithm. The data is automatically fed to the machine learning system and used to train the model. It tries to find a particular system and provides the desired solution. The difference is that the work is done by a machine and not a human. Some of the algorithms used in unsupervised machine learning are singular value decomposition, hierarchical clustering, least squares, principal component analysis, fuzzy methods, and more.
Reinforcement ML is very similar to traditional reinforcement learning. Here, the machine uses an algorithm to find data through a process called trial and error. After that, the system itself chooses the method that will work best with the best results. There are three elements involved in machine learning: the agent, the environment, and the action. A facilitator is someone who is a learner or decision maker. Environment is the atmosphere in which the agent interacts, and action is considered as the activity that the agent performs. This happens when the agent chooses the best option and continues to adopt it.
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