The concept of artificial intelligence began at the Dartmouth Conference in 1956, and due to the influence of data, computing power, intelligent algorithms and other factors, the development of artificial intelligence technology and application has experienced many highs and lows.
Since 2022, the large model represented by ChatGPT has exploded overnight, it can generate answers based on the patterns and statistical laws seen in the pre-training stage, and can also interact according to the context of the chat, truly chat and communicate like a human, and even complete tasks such as writing emails, scripts, copywriting, translation, and writing. Artificial intelligence technology has suddenly entered a new phase.
Data, computing power and algorithms are considered to be the three core elements of artificial intelligence development, data is the foundation, algorithms are the core, and computing power is the support.
All machine Xi models are designed to learn Xi a function (f) that provides the most precise correlation between the input value (x) and the output value (y). y=f(x)
Most commonly, we have some historical data x and y, and we can deploy an AI model to provide the best mapping between these values. The result cannot be 100% accurate, otherwise it will be a simple mathematical calculation that does not require machine Xi. Instead, the f function we trained can be used to use the new x to the new y, thus enabling the analysis. Various models of machine Xi achieve this result by employing different methods, which is the basic principle of machine Xi.
The number of problems faced in reality is enormous, and the machine Xi models used to solve them are also diverse, as some algorithms are better at handling certain types of problems than others. Therefore, we need to have a clear understanding of the pros and cons of each algorithm, and today we have listed 10 of the most popular AI algorithms that we hope will help you.
To date, linear regression has been used in mathematical statistics for more than 200 years. The gist of the algorithm is to find the value of the coefficient (b) that has the greatest impact on the accuracy of the function f we are trying to train. The simplest example is y= b0 + b1 * x, where b0 + b1 is the function in question.
By adjusting the weights of these coefficients, data scientists can obtain different training results. The core requirements for the success of the algorithm are to have clear data without much noise (low-value information) and to remove input variables with similar values (related input values).
Linear regression is all about finding a straight line and fitting it as closely as possible to the data points in the scatter plot. It attempts to represent both the independent variable (x-value) and the numerical result (y-value) by fitting the straight-line equation to this data. Then you can use this line to ** future values!
The most commonly used technique for this type of algorithm is least of squares. This method calculates the best-fit line so that the perpendicular distance to each data point on the line is minimized. The total distance is the sum of the squares of the vertical distances (green lines) of all data points. The idea is to fit the model by minimizing this squared error or distance.
Linear regression algorithms are often used to optimize gradient descent for statistics in finance, banking, insurance, healthcare, marketing, and other industries. Logistic regression is another popular AI algorithm that is capable of delivering binary results. This means that the model can both result and specify one of two classes of y-values. This function is also based on changing the weights of the algorithm, but it is different due to the use of nonlinear logic functions to transform the results. The function can be represented as an s-shaped line that separates the true value from the false value. 
The requirements for success are the same as for linear regression – remove input samples of the same value and reduce the amount of noise (low-value data). This is a very simple function that can be mastered relatively quickly and is ideal for performing binary classification. This is one of the oldest, most commonly used, simplest, and most efficient models of machine Xi. It is a classic binary tree where a "yes" or "no" decision is made for each split until the model reaches the result node.
In this algorithm, the training model learns Xi Xi the value of the target variable by learning the decision rules of the tree representation. A tree is made up of nodes with corresponding attributes. At each node, we ask questions about the data based on the available characteristics. The left and right branches represent possible answers. The final node (i.e., the leaf node) corresponds to a ** value.
The importance of each feature is determined through a top-down approach. The higher a node, the more important its properties become. 
The model is easy to learn, does not require data standardization, and is commonly used for regression and classification tasks. Naive Bayes are based on Bayes' theorem. It measures the probability of each class, and the conditional probability of each class gives a value of x. This algorithm is used to classify problems and obtain a binary "yes or no" result. It's a simple but very powerful model for solving a variety of complex problems. It calculates two types of probabilities: one appearance per class.
Assuming there is an additional x modifier, the conditional probability of an independent class.
This model is known as a naïve model because it operates on the assumption that all input data values are independent of each other. While this is not possible in the real world, this simple algorithm can be applied to a large number of standardized data streams to achieve highly accurate results. A support vector machine (SVM) is a supervised algorithm for classifying problems. The support vector machine attempts to draw two lines between the data points, with the largest margin between them. To do this, we plot the data item as a point in n-dimensional space, where n is the number of input features. On this basis, the support vector machine finds an optimal boundary, called the hyperplane, which optimally separates the possible outputs by class labels.
The distance between the hyperplane and the nearest class point is called the margin. The optimal hyperplane has the largest boundary and can classify points, maximizing the distance between the nearest data point and the two classes.
The best hyperplane is the one that has the largest positive vectors and separates most of the data nodes. This is an extremely powerful classifier that can be applied to a wide range of data standardization problems. This is a very simple but very powerful machine Xi model that uses the entire training dataset as the presentation field. The resulting value is calculated by examining k data nodes (so-called neighbors) with similar values throughout the dataset and using a Euclidean number (which can be easily calculated based on the difference in values) to determine the resulting value. The resulting value is calculated by examining k data nodes (so-called neighbors) with similar values throughout the dataset and using a Euclidean number (which can be easily calculated based on the difference in values) to determine the resulting value. 
Such datasets can require significant computational resources to store and process data, suffer from loss of accuracy when multiple attributes are present, and must be constantly collated. However, they work very fast and are very accurate and efficient when finding the desired value in large datasets.
K-means are clustered by classifying datasets. For example, this algorithm can be used to group users based on their purchase history. It finds k clusters in the dataset. k- The mean is used for unsupervised Xi, so we only need to use the training data x and the number of clusters k we want to identify.
The algorithm iteratively assigns each data point to one of k groups based on the characteristics of each data point. It selects k points for each k-cluster, called the centroid. Based on similarity, new data points are added to the cluster with the nearest centroid. This process continues until the center of mass stops changing.
The basic idea of this algorithm is that the opinions of many people are more accurate than the opinions of individuals. To classify the new objects, we vote from each decision tree, combine the results, and then make the final decision based on a majority vote. A stochastic decision forest consists of a decision tree in which multiple data samples are processed by the decision tree and the results are aggregated (like collecting many samples in a bag) to find a more accurate output value.
Instead of looking for one optimal route, define multiple suboptimal routes, resulting in a more precise overall result. If a decision tree solves the problem you're after, then a random forest is a tweak to the method that can provide better results. Due to the sheer volume of data we are able to capture today, the problem of machine Xi becomes even more complex. This means that training is extremely slow and it is difficult to find a good solution. This problem is often referred to as the "curse of dimensionality".
Dimensionality reduction attempts to solve this problem by combining specific features into higher-level features without losing the most important information. Principal component analysis (PCA) is the most popular dimensionality reduction technique.
Principal component analysis reduces the dimensionality of a dataset by compressing it into a low-dimensional line or hyperplanar subspace. This preserves as much as possible the distinctive features of the original data. 
An example of dimensionality reduction that can be achieved by approximating all data points to a straight line. A neural network is essentially a set of interconnected layers of edges and nodes with weights, called neurons. It uses the output features of the previous layer as the input of the next layer for feature Xi, and maps the features of the existing space samples to another feature space after the layer-by-layer feature mapping, so as to learn Xi have better feature representation of the existing input.
Deep neural networks have multiple feature transformations of nonlinear mappings that can fit highly complex functions. If the deep structure is seen as a network of neurons, the core idea of the deep neural network can be described in three points as follows:
1) The pre-training of each layer of the network adopts unsupervised learning Xi; (2) unsupervised Xi train each layer layer layer by layer, that is, the output of the previous layer is used as the input of the next layer; (3) Supervised Xi to fine-tune all layers (plus a classifier for classification). The main difference between deep neural networks and traditional neural networks is the training mechanism. In order to overcome the shortcomings of traditional neural networks, such as easy overfitting and slow training speed, deep neural networks generally adopt a layer-by-layer pre-training training mechanism, rather than the backpropagation training mechanism of traditional neural networks.
Advantages: It overcomes the shortcomings of time-consuming and laborious manual design of features;
The primary features of each layer are obtained through the pre-training of layer-by-layer data.
Distributed data Xi is more effective (exponential);
Compared with shallow modeling, deep modeling can represent the actual complex nonlinear problems in a more detailed and efficient manner.
DNN is one of the most widely used algorithms Xi artificial intelligence and machine learning. Significant improvements have been made in text and speech applications based on deep chemistry Xi, deep neural networks for machine perception and OCR, the use of deep chemistry Xi to enhance strong chemical Xi and robotic motion, and other miscellaneous applications of DNNs. As you can see, there is a wide variety of AI algorithms and machine learning Xi models. Some are better suited for data classification, while others excel in other areas. There is no one algorithm that fits all, so it's crucial to choose the one that best suits your situation.