machine learning course on coursera

linear kernel)The neural network works well in all kinds of n, m cases, and the defect is that the training speed is slow.Reference documents[1] Andrew Ng Coursera public class seventh week[2] Kernel Functions for machine learning applications. http://crsouza.com/2010/03/kernel-functions-for-machine-

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) Bui

corresponding to the numerical solution. Therefore, this solution is not the smallest solution that is solved step by step, as mentioned earlier by the PLA algorithm.Answer is the reason for more emphasis on the results, the direct solution is the mathematical derivation of the exact solution, so that the minimum solution is obtained, in line with the solution conditions, but also to solve the pseudo-inverse algorithm (this method is called Gaussian elimination method, see also Gauss, looked at

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

intervention on the results of model training it's a lever. Model does not understand the business, really understand the business is people. What the model can do is to learn from the cost function and sample, and find the optimal fit of the current sample. Therefore, machine learning workers should be appropriate to the needs of the characteristics of some human intervention and "guidance", such as the h

Today we share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-exercise solution for job three. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answers, so I would like to do their own ques

don't score. (It's worth noting that each user is required to build his own regression model.)From another point of view, it is also possible to give each user the degree of preference for a particular film (i.e. weight), then learn the characteristics of each film, and finally use the regression to predict those who have not been rated film.Of course, it is also possible to optimize the level of each user's passion for different types of movies and

introductory books. We recommend an article to further discuss this topic: "The best entry-level learning resources for machine learning". Related overview video: You can also watch some popular machine learning speeches. Example: Interview with Tom Angel El and Peter norv

Reprint Please specify the Source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectoryMachine learning Cornerstone Note When machine learning can be used (1)Machine learning Cornerstone Note 2--When you can use machine

Hello everyone, I am mac Jiang, first of all, congratulations to everyone Happy Ching Ming Festival! As a bitter programmer, Bo Master can only nest in the laboratory to play games, by the way in the early morning no one sent a microblog. But I still wish you all the brothers to play happy! Today we share the coursera-ntu-machine learning Cornerstone (Machines

Hello everyone, I am mac Jiang, today and you share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-job four of the exercise solution. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answer

First, the foregoingNumPy(numerical python abbreviation) is an open source Python Scientific Computing Library. Use NumPy , you can use arrays and matrices in a very natural way . Numpy contains many useful mathematical functions, including linear algebra operations, Fourier transforms, and random number generation functions . The Library's predecessor was a library for array operations that began in 1995 years. After a long period of development, it has basically become the most basic Python

of the current node is the middle half of the distance of all its leaf nodes is float (NUMLEAFS)/2.0/plottree.totalw* 1, but since the start Plottree.xoff assignment is not starting from 0, but the left half of the table, so also need to add half the table distance is 1/2/plottree.totalw*1, then add up is (1.0 + float (numleafs))/2.0/ Plottree.totalw*1, so the offset is determined, then the X position becomes Plottree.xoff + (1.0 + float (numleafs))/2.0/PLOTTREE.TOTALW3, for Plottree function p

. Classification model 1) training, testing. 2 Common methods: Naive Bayesian, maximum entropy, SVM. 6. Evaluation indicators 1) Accuracy rate Accuracy = (TP + TN)/(TP + FN + FP + TN) reflects the ability of the classifier to judge the whole sample--------------------positive judgment, negative judgment negative. 2) Accuracy rate Precision = tp/(TP+FP) reflects the proportion of the true positive sample in the positive case determined by the classifier 3) Recall rate Recall = tp/(TP+FN) reflec

learning is a discipline that studies how to use machines to simulate human learning activities. Machine Learning is a learning that studies machines to acquire new knowledge and new skills and to recognize existing knowledge. The "mach