edureka machine learning course

reduced after removing the label, (2) using the data of the reduced dimension to train the model, (3) for the new data points, the PCA reduced dimension to obtain the dimensionality reduction data, and the model to obtain the predicted value. Note : You should only use the training set data for PCA dimensionality reduction get Map $x^{(i)}\rightarrow z^{(i)}$, and then apply the mapping (PCA-selected principal matrix $u_reduce$) to the validation set and test set do not use PCA to block ove

ADD1 () DROP1 () 9. Regression Diagnostics Does the sample conform to the normal distribution? Normality test: function shapiro.test (X$X1) The distribution of normality Learning set/Is there outliers? How to find Outliers is the linear model reasonable? Maybe the relationship between nature is more complicated. Whether the error satisfies the independence, equal variance (the error is no

classifier will be severely affected, as shown in:To solve the above two problems, we adjust the optimization problem to:Note: When ξ>1, it is possible to allow the classification to be wrong, and then we add the ξ as a penalty to the target function.Using Lagrange duality again, we get the duality problem as:Surprisingly, after adding the L1 regularization item, only a αi≤c is added to the like limit in the dual problem. Note that the b* calculation needs to be changed (see Platt's paper)KKT d

This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).The following is a measure of purity:Finally, when to stop:Decision tree may be overfitting, reducing the number of Ein and leaves (indicating the complexity

In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains why you can use linear regression or a logistic regression to replace linear classificationThen the stochastic gradient descent method is introduced, which is an improvement to the gradient descent method, which greatly improves the efficiency.Finally

This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the more similar.Next is the polynomial Kernel, w

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit th

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

rigorously, because one of the objective functions in statistical learning is to maximize the prediction of the correct expected probability, we only consider the common loss function. Loss function is an important index to approximate the quality of the model, the greater the value of the loss function is, the greater the prediction error of the model, so what we need to do is to update the parameters of the model and minimize the value of the loss

) The principle of big data Large data rationale Large amounts of data can greatly improve the final performance of the learning algorithm, rather than whether you use more advanced algorithms, etc., so there is a sentence: "It's not a who had the best algorithm that wins. It's Who's have the most data. Of course, based on the two-point premise hypothesis: 1. Assume that the characteristics of the sample ca

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-

Directory 1. Introduction 1.1. Overview 1.2 Brief History of machine learning 1.3 Machine learning to change the world: a GPU-based machine learning example 1.3.1 Vision recognition based on depth neural network 1.3.2 Alphago 1.3.

The motive and application of machine learningTools: Need genuine: Matlab, free: Octavedefinition (Arthur Samuel 1959):The research field that gives the computer learning ability without directly programming the problem.Example: Arthur's chess procedure, calculates the probability of winning each step, and eventually defeats the program author himself. (Feel the idea of using decision trees)definition 2(Tom

, the minimum value of the price function jval provided by us, of course, returns the solution of the vector θ. The above method is obviously applicable to regular logistic regression.5. Conclusion Through several recent articles, we can easily find that both linear regression and logistic regression can be solved by constructing polynomials. However, you will gradually find that more powerful non-linear classifiers can be used to solve polynomial reg

Machine learning is a comprehensive and applied discipline that can be used to solve problems in various fields such as computer vision/biology/robotics and everyday languages, as a result of research on artificial intelligence, and machine learning is designed to enable computers to have the ability to learn as humans

As an article of the College (http://xxwenda.com/article/584), the follow-up preparation is to be tested individually. Of course, there have been many tests. Apache Spark itself1.MLlibAmplabSpark was originally born in the Berkeley Amplab Laboratory and is still a Amplab project, though not in the Apache Spark Foundation, but still has a considerable place in your daily GitHub program.ML BaseThe mllib of the spark itself is at the bottom of the three

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf

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 vector machines), clustering, dimensionality reduc