edureka machine learning course

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this pi

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/1kVNe5EJ 1. course Introduction 2.

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

Original address: http://blog.csdn.net/lrs1353281004/article/details/79529818 Sorting out the machine learning-algorithm engineers need to master the basic knowledge of machine learning, and attached to the internet I think that write a better blog address for reference. (Continuous update)

design a system that allows it to learn in a certain way based on the training data provided; With the increase of training times, the system can continuously learn and improve the performance, through the learning model of parameter optimization, it can be used to predict the output of related problems. 4. Machine Learning Algorithm Classification: (1) Supervi

Recently saw a relatively good machine learning course, roughly heard it again. The overall sense of machine learning field is still more difficult, although Li Hongyi teacher said is very good, not enough to absorb up or have a certain difficulty. Even though the process ha

first, gradient descent method In the machine learning algorithm, for many supervised learning models, the loss function of the original model needs to be constructed, then the loss function is optimized by the optimization algorithm in order to find the optimal parameter. In the optimization algorithm of machine

, as shown in: Step 4: run the model. After completing the preceding operations, you can run the program. Click "run" at the bottom to run the model. After each module is run, a green check box is displayed in the upper right corner, if an error occurs in each module or step, a red icon will appear in the same place. After you move the mouse over it, an error type will be displayed. Step 5: view the result. Right-click the dot in the "Evaluate Model" box and select "Visualize" to view the mode

clearly explained. It also covers De Rham cohomology and Lie algebra, where audience is invited to discover the beauty by linking geometry wi Th algebra.Modern Graph theoryBela BollobasIt is a modern treatment of this classical theory, which emphasizes the connections and other mathematical subjects--fo R example, random walks and electrical networks. I found some messages conveyed by the This book was enlightening for my all in machine

) Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell. A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA. (notice here not to be confused with the implied Dirichlet distribution (latent Dirichlet allocation), although

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

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption Starting from 0, 0 (or any other valu

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

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

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

watch all the course videos at any time, download handouts and notes from Stanford CS229 course. This course includes homework and small tests, which mainly explain the knowledge of linear algebra, using the Octave library. Caltech learning from data at the California Institute of Technology: You can ta

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

. 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

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