What is Machine Learning?

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Machine learning is a field that studies algorithms that allow computers to learn on their own to solve problems. Computers can learn from data and make predictions or decisions based on their learning results. Basic principles of machine learning The basic principle of machine learning is to train a model through data and predict (test) results for new data through the model. At this time, the learning data consists of input data and the correct answer, that is, a label. Types of Machine Learning Machine learning can be broadly divided into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method of training a model using labeled training data, while unsupervised learning is a method of training a model using unlabeled data. Reinforcement learning is a method of learning optimal actions for decision making. 3 types of machine learning 3 types of machine learning (Source: https://towardsdatascience.com/i ... inners-eed6024fdb08) The need and applications of machine learning Machine learning is used in many fields. This is due to the various advantages and characteristics of machine learning.

The need for machine learning Machine learning is useful in handling big data and is also used to solve complex problems that are difficult for humans to solve. Additionally, machine learning can be used to create predictive models, which can be used to predict specific results. Applications  Special Data  of Machine Learning Machine learning is used in a variety of fields. For example, in the medical field, it is used to predict a patient's health status, or in the financial field, it is used to predict stock prices. Additionally, it is used in various fields such as self-driving cars, voice recognition, and facial recognition. Understanding supervised and unsupervised learning I mentioned three types of machine learning earlier, and among them, supervised learning and unsupervised learning are the most widely used concepts. Supervised learning and unsupervised learning can be distinguished depending on what data are learned. What is supervised learning? Supervised learning is a learning method that uses labeled training data.




In other words, input data and the correct answer, i.e., a label, are given together, and the model is trained through this. The model trained in this way can make predictions for new input data. An example of supervised learning – regression Regression is predicting continuous values. Based on the learned data, you can predict the price of an item, a student's grade, etc. Examples of Supervised Learning – Classification Since learning is done with data that has the correct answer, classification work can be done based on this. In this case, classifying the results into two is called binary classification, and classifying more results is called multiclass classification. regression vs classification Difference between Regression and Classification (Source: https://turbofuture.com/industri ... stic-Classification) What is unsupervised learning? Unsupervised learning is a learning method that uses unlabeled training data. In other words, only input data is given, and the goal is to find patterns or structures in the data.