list of machine learning models

Machine learning Algorithms Study NotesGochesong@ Cedar CedroMicrosoft MVPThis series is the learning note for Andrew Ng at Stanford's machine learning course CS 229.Machine learning Al

not an easy-to-debug because of that compilation layer. Nltk-it is a natural language processing tool with very unique and salient features. It also includes some basic classifiers like Naive Bayes. If your work was about text processing This is the right tool to process data. Other Libraries – (this list is being constantly updated.) Deep learning Libraries PYLEARN2-"A

, the milk is in the same place with the bread sold higher, or with other goods sold higher. Data mining technology can be used to solve such problems. In particular, the store of goods in supermarkets can be divided into related analysis class scene. In daily life, the application of data mining technology is very extensive. For example, for a merchant, it is often necessary to classify their customers ' grades (SVIP, VIP, ordinary customers, etc.), which can be used as training data and part o

I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating.1. Test and Training error: Why lower training error was not always a good thing:esl figure 2.11. Test and training error as a function of model complexity.2. Under and overfitting: PRML figure 1.4. Plots of polynomials has

difference is far from the real curve (Green Line). Here is an over-fitting problem ), it can be said that it is a very important issue in machine learning. Root-mean-square error We can see the example in Figure 1.5. After M reaches a certain stage, the error on the test data will increase significantly. We understand it as overfitting! Let's briefly discuss over-fitting. There are many factor

). Here is an over-fitting problem ), it can be said that it is a very important issue in machine learning. Root-mean-square error We can see the example in Figure 1.5. After M reaches a certain stage, the error on the test data will increase significantly. We understand it as overfitting! Let's briefly discuss over-fitting. There are many factors that cause over-fitting. I personally understand that ther

Machine Learning notes of the Dragon Star program 　Preface In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. This course chooses to talk about the basic mod

Original: Image classification in 5 MethodsAuthor: Shiyu MouTranslation: He Bing Center Image classification, as the name suggests, is an input image, output to the image content classification of the problem. It is the core of computer vision, which is widely used in practice. The traditional method of image classification is feature description and detection, such traditional methods may be effective for some simple image classification, but the traditional classification method is overwhelmed

Course Description:This is the last lesson of the course, the author first summed up the theory, methods, models, paradigms, and so on machine learning. Finally, the application of Bayesian theory and Aggregation (aggregation) method in machine learning is introduced. Course

. Although neither the categories nor the algorithms are exhaustive, I think they are representative and contribute to a general understanding of the whole field.Regression analysis　　Regression is a modeling method that determines the amount of prediction errors for a model, and then iteratively optimizes the relationship between the variables by this amount. Regression method is the main application of statistics, which is classified as statistical machine