unsupervised machine learning tutorial

For a given set of data and problems, the machine learning method to solve the problem is generally divided into 4 steps: A Data preprocessing First, you must ensure that the data is in a format that meets your requirements. The standard data format can be used to fuse algorithms and data sources to facilitate matching operations. In addition, you need to prepare specific data formats for

First, the machine learning algorithm engineers need to master the skills Machine Learning algorithm engineers need to master skills including (1) Basic data structure and algorithm tree and correlation algorithm graph and correlation algorithm hash table and correlation algorithm matrix and correlation algorithm

. 2. How to classify real samples: Iris DataSet, which is a very classic dataset, Scikit-learn the Basic sample datasets commonly used in tutorial. This paper focuses on the cross-validation (Zhouhuazhi-machine learning, which is a good summary of the model evaluation). Error: Training error, test error, generalization error. Our ultimate goal

Types of learning according to my personal understanding, the classification of learning methods in machine learning helps us face a specific problem, you can select an appropriate machine learning algorithm based on your goals. F

inference algorithm (Graph inference) or Laplace support vector machine (Laplacian SVM).1.4 Intensive Learningin this learning mode, input data as feedback to the model, unlike the monitoring model, the input data is only as a check model of the wrong way, under the reinforcement learning, the input data directly feedback to the model, the model must be immediat

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vecto

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vecto

supervised and unsupervised learning, and stepping into core technologies such as classification, regression, clustering, and dimensionality reduction, and then explaining the more commonly used and classic algorithms, as well as advanced content such as feature selection and model validation. After completing this tutorial, participants will have a clearer unde

is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine