where to start machine learning

Microsoft Azure cloud service introduces the machine learning module. Users only need to upload data and use some algorithm interfaces and R or other language interfaces provided by the machine learning module, you can use Microsoft Azure's powerful cloud computing capabilities to implement your

Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally converted to the problem of solving the alpha of the Child variable of the Laplace multiplication

Knowing an algorithm and using an algorithm are two different things. What should I do if I find that the model has a big error after you train the data? 1) Obtain more data. It may be useful. 2) reduce feature dimensions. You can manually select one or use mathematical methods such as PCA. 3) Obtain more features. Of course, this method is time-consuming and not necessarily useful. 4) add polynomial features. Are you trying to save your life? 5) Build your own, new, and better features. A litt

The essential difference between classification and clustering in machine learning There are two kinds of big problems in machine learning, one is classification, the other is clustering.In our life, we often do not have too much to distinguish between these two concepts, think clustering is classification, classificat

Machine Learning 4, machine learning Probability-based classification method: Naive BayesBayesian decision theory Naive Bayes is a part of Bayesian decision-making theory. Therefore, before explaining Naive Bayes, let's take a quick look at Bayesian decision-making theory knowledge. The core idea of Bayesian decision-m

In machine learning, often need to calculate the distance between each sample, used for classification, according to distance, different samples grouped into a class; But in the current machine learning algorithm, the distance calculation mode is endless, then this blog is mainly to comb the current

A brief introduction to Learning _note1 against Sample machine Machine learning methods, such as SVM, neural network, etc., although in the problem such as image classification has been outperform the ability of human beings to deal with similar problems, but also has its inherent defects, that our training sets are fe

The Ames Razor principle (Occam ' s Razor)One sentence is said, "an explanation of the data should is mad as simple as possible,but no simpler".The meaning of machine learning is that the simplest explanation of the data is the best explanation (the simplest model, fits the data is also and the most plausible).For example, the picture above, the right is not better than the left to explain? That's obviously

are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508 Training errors, cross-validation errors, and relationships between lambda graphs are represented as follows:When th

Machine learning practices in python3.x and python machine learning practices Machine Learning Practice this book is written in the python2.x environment, while many functions and 2 in python3.x. the names or usage methods in x ar

1. Decision Tree　　applicable conditions: The data of different class boundary is non-linear, and by continuously dividing the feature space into a matrix to simulate. There is a certain correlation between features. The number of feature values should be similar, because the information gain is biased towards more numerical characteristics.　　Advantages: 1. Intuitive decision-making rules; 2. Nonlinear characteristics can be handled; 3. The interaction between variables is considered.　　Disadvanta

Recommended BooksHere is a list of books which I had read and feel it was worth recommending to friends who was interested in computer Scie nCE.Machine Learningpattern recognition and machine learningChristopher M. BishopA new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Ba Yesian perspective. I

1. What is machine learningMachine learning is the conversion of unordered data into useful information.The main task of machine learning is to classify and another task is to return.Supervised learning: It is called supervised learning