(Linear Regression) is probably the most popular machine learning algorithm. Linear regression is to find a straight line and make this straight line fit the data points in the scatter plot as closely as possible. It attempts to represent the independent variables (x values) and numerical results (y values) by fitting a straight line equation to this data. This line can then be used to predict future values!

The most commonly used technique for this algorithm is the least squares method. This method calculates a line of best fit that minimizes the perpendicular distance from each data point on the line. The total distance is the sum of the squares of the vertical distances (green line) of all data points. The idea is to fit the model by minimizing this squared error or distance.

For example, predict next year's housing price growth, next quarter's sales of new products, etc. It doesn't sound difficult, but the difficulty of the linear regression algorithm is not to obtain the predicted value, but to be more accurate. For that possibly very small number, how many engineers have spent their youth and hair on it.

Logistic regression is similar to linear regression, but the result of logistic regression can only have two values. If linear regression is predicting an open value, logistic regression is more like doing a yes or no question.

Logistic regression is often used by e-commerce or takeout platforms to predict users' purchasing preferences for categories.

If linear and logistic regression both end the task in one round, then decision trees (Decision Trees) are a multi-step action. They are also used in regression and classification tasks, but the scenarios are usually more complex and specific.

To give a simple example, when a teacher faces students in a class, who are the good students? It seems too crude to simply judge that a student with a score of 90 on the exam is considered a good student, and cannot be based on scores alone. For students whose scores are less than 90 points, we can discuss them separately from aspects such as homework, attendance, and questions.

Naive Bayes is based on Bayes' theorem, which is the relationship between two conditions. It measures the probability of each class, the conditional probability of each class given the value of x. This algorithm is used in classification problems and yields a binary yes/no result. Take a look at the equation below.

Naive Bayes classifier is a popular statistical technique with a classic application in spam filtering

To explain Bayes' theorem in non-terminology is to use the probability of B to occur under condition A to obtain the probability of A occurring under condition B. For example, if a kitten likes you, there is a% chance that it will turn its belly in front of you. What is the probability that the kitten will like you if it turns its belly in front of you? Of course, doing this question is tantamount to scratching the surface, so we also need to introduce other data. For example, if the kitten likes you, there is a b% chance of sticking to you, and a c% chance of purring. So how do we know the probability that a kitten likes us? Through Bayes’ theorem, we can calculate it from the probability of belly turning, sticking and purring.

Support Vector Machine (SVM) is a supervised algorithm for classification problems. A support vector machine attempts to draw two lines between data points with the largest margin between them. To do this, we plot data items as points in n-dimensional space, where n is the number of input features. On this basis, the support vector machine finds an optimal boundary, called a hyperplane, which best separates 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 margin that classifies points such that the distance between the nearest data point and the two classes is maximized.

Therefore, the problem that support vector machines want to solve is how to separate a bunch of data. Its main application scenarios include character recognition, facial recognition, text classification and other recognitions.

The K-Nearest Neighbors (KNN) algorithm is very simple. KNN classifies objects by searching the entire training set for the K most similar instances, or K neighbors, and assigning a common output variable to all these K instances.

The choice of K is critical: smaller values ​​may give a lot of noise and inaccurate results, while larger values ​​are infeasible. It is most commonly used for classification, but is also suitable for regression problems.

The distance used to evaluate the similarity between instances can be Euclidean distance, Manhattan distance, or Minkowski distance. Euclidean distance is the ordinary straight-line distance between two points. It is actually the square root of the sum of the squares of the difference in point coordinates

KNN theory is simple and easy to implement, and can be used for text classification, pattern recognition, cluster analysis, etc.

K-means clusters the data set by classifying it. For example, this algorithm can be used to group users based on purchase history. It finds K clusters in the data set. K-means is used for unsupervised learning, so we only need to use the training data X, and the number of clusters we want to identify K.

The algorithm iteratively assigns each data point to one of K groups based on its characteristics. It selects K points for each K-cluster (called centroids). Based on similarity, new data points are added to the cluster with the closest centroid. This process continues until the center of mass stops changing.

In life, K-means plays an important role in fraud detection and is widely used in the fields of automobile, medical insurance, and insurance fraud detection.

Random Forest is a very popular ensemble machine learning algorithm. The basic idea of ​​this algorithm is that the opinions of many people are more accurate than the opinions of one individual. In a random forest, we use an ensemble of decision trees (see Decision Trees).

Random forest has a wide range of application prospects, from marketing to health care insurance. It can be used to model marketing simulations, count customer sources, retention and loss, and can also be used to predict disease risks and patient susceptibility. .

Principal component analysis reduces the dimensionality of a data set by compressing it into low-dimensional lines or hyperplanes/subspaces. This preserves as much of the salient features of the original data as possible.

Artificial Neural Networks (ANN) can handle large and complex machine learning tasks. A neural network is essentially a set of interconnected layers composed of weighted edges and nodes, called neurons. Between the input layer and the output layer, we can insert multiple hidden layers. Artificial neural networks use two hidden layers. Beyond that, deep learning needs to be dealt with.

Artificial neural networks work similarly to the structure of the brain. A group of neurons is given a random weight to determine how the neuron processes the input data. The relationship between input and output is learned by training a neural network on input data. During the training phase, the system has access to the correct answers.

If the network doesn't accurately recognize the input, the system adjusts the weights. After sufficient training, it will consistently recognize the correct patterns.

Image recognition is a well-known application of neural networks.