Overview and classification of machine learning algorithms

A machine learning algorithm is a computer program that can learn from data. It is able to extract information from collected data and use this information to improve the performance of tasks. To ensure accuracy, the algorithm needs to be fully trained.

How does the machine learning algorithm work? Understanding the basic concepts of artificial intelligence will give you a clearer answer.

Artificial intelligence is a broad term used to describe the ability of computers to imitate human intelligence. It covers a variety of technologies, including machine learning, natural language processing, and computer vision. Although AI can exhibit human-like cognitive abilities, the way it operates is inherently different from the human mind. Artificial intelligence makes reasoning and decisions through algorithms and data, while human thinking is affected by multiple factors such as perception, emotion, and experience. Therefore, although artificial intelligence can reach or even surpass human levels on some tasks, it still has its limitations.

Artificial intelligence exists in the form of computer programs, using programming languages ​​​​such as Python and Java, and aims to write complex programs to simulate human cognitive processes. Among them, machine learning algorithms are programs that can accurately reconstruct learning and cognitive processes.

In this process, the machine learning algorithm solves the problem by being exposed to various variables and looking for the best combination between them. Therefore, machine learning algorithms need to "learn" this specific combination of variables through repeated trials with large amounts of data and find the optimal solution.

Machine learning algorithms require large amounts of data to improve their effectiveness in solving problems, and the algorithms continue to improve and iterate themselves with each problem solved.

How to create a machine learning algorithm

In order to make the algorithm self-learning, we can take a variety of methods. Generally speaking, the first step to start creating a machine learning algorithm is to define the problem. This includes trying to find a solution to the problem, describing the boundaries of the problem, and focusing on the most basic problem statement.

After defining the problem, the data needs to be cleaned and analyzed to find a solution. Every machine learning problem has an associated dataset.

Algorithms often imitate human cognitive methods. After cleaning the data and making it readable for machine learning algorithms, the data must be preprocessed, which increases the accuracy of the final solution.

For example, in the provided example of an algorithm for analyzing cat images, the program is taught to analyze changes in the color of the image and how the image changes. If the color suddenly switches from one pixel to another, it may represent the outline of a cat. In this way, the algorithm can find the edges of the cat in the picture. Using these methods, ML algorithms can be tuned until they can find optimal solutions in small data sets.

Once this step is completed, the target function will be introduced. The objective function makes the algorithm do its job more efficiently. Although the goal of the cat detection algorithm is to detect cats, the objective function is to solve the problem in the shortest time. By introducing an objective function, the algorithm can be specifically tuned so that it finds a solution faster and more accurately.

Types of machine learning algorithms

1. Supervised machine learning

Supervised machine learning is training machine learning Algorithm is the most widely used method because its algorithm is relatively simple. Supervised machine learning learns from a small data set called a training data set. This knowledge is then applied to a larger data set, called the problem data set, resulting in a solution. The data fed into these machine learning algorithms is labeled and classified to make it understandable, so a lot of human effort is required to label the data.

2. Unsupervised machine learning algorithm

The unsupervised machine learning algorithm is the opposite of the supervised algorithm. The data fed to unsupervised machine learning algorithms is neither labeled nor classified. This means requiring machine learning algorithms to solve problems with minimal manual training. These algorithms are given a dataset and left to their own devices, which allows them to create a hidden structure. Hidden structures are essentially patterns of meaning in unlabeled data sets.

3. Reinforcement Learning Algorithm

This is a new type of machine learning algorithm. Reinforcement learning algorithms provide rewards for algorithms when they provide correct solutions, and reward them when the solutions are incorrect. Rewards are removed. More efficient solutions also provide higher rewards to the reinforcement learning algorithm, which then optimizes its learning process to maximize rewards through trial and error.

Popular Machine Learning Algorithms

Some of the most popular machine learning algorithms include:

Linear Regression: Linear regression is a supervised learning algorithm used to find the straight line that best fits a set of data points.

Logistic Regression: Logistic regression is a supervised learning algorithm used to classify data points into two categories.

Support Vector Machine: Support vector machine is a supervised learning algorithm used for classification and regression tasks.

Decision Tree: Decision tree is a supervised learning algorithm used for classification and regression tasks.

Random Forest: Random Forest is an ensemble learning algorithm used for classification and regression tasks.

K nearest neighbor: K nearest neighbor is a supervised learning algorithm used for classification and regression tasks.

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