edureka machine learning course

outside world. Of course this is also relative, but in order to achieve our goal, I will delimit the boundary, when we write our own matrix model, data frame or build our own database, we will use Python in the NumPy, Panda and Matplotlib library. In some cases, we won't even use the full functionality of these libraries. We'll talk about it later, so let's put their names in the first place for a better understanding. The features that come with you

question is, how do you choose the right algorithm for your problem? Microsoft provides us with a good guide inMicrosoft Azure machine learning algorithm Cheat Sheet. This is a selection flowchart, the approximate process text is described as follows: Do you want to predict the future data points If no, then select the aggregation algorithm (only the k nearest neighbor algorithm is optional)

(i) Recognition of the returnRegression is one of the most powerful tools in statistics. Machine learning supervised learning algorithm is divided into classification algorithm and regression algorithm, in fact, according to the category label distribution type is discrete, continuity and defined. As the name implies, the classification algorithm is used for disc

Course Description:This is the last lesson of the course, the author first summed up the theory, methods, models, paradigms, and so on machine learning. Finally, the application of Bayesian theory and Aggregation (aggregation) method in machine

Machine learning notes (b) univariate linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. Model representationHow to solve the problem of house price in note (a), this will be

Long time no See, Hulu machine learning questions and Answers series and updated again!You can click "Machine Learning" in the menu bar to review all the previous installments of this series and leave a message to express your thoughts and ideas, and perhaps see your testimonials in the next article.Today's theme is"Di

million points to find an optimal hyper-plane, where there are 100 supporting vectors, then I just need to remember the information of these 100 points, and for subsequent classifications it is only necessary to use these 100 points instead of all 1 million points for calculation. Of course, in addition to the "memory-based learning" algorithm such as K-nearest neighbor, usually the algorithm does not dire

ability of Model: model's ability to predict unknown dataFit: Machine learning model in the course of training, by updating the parameters, so that the model continues to fit the observable data (training set) processStates of two models with overfitting and under-fitting are presentRegularization methods (regularization of L1 and L2 regularization)The aim of re

In-depth spark machine learning combat (user behavior analysis)Course View Address: http://www.xuetuwuyou.com/course/144The course out of self-study, worry-free network: http://www.xuetuwuyou.comI. Objectives of the courseMaster the various operations of sparksql in-depth un

crawler Introduction, do not have to read too many books, online resources a lot of, of course, my csdn web crawler column , or quite popular:Tutorial Address: Click to viewBook Resources recommended:1. Want to learn the network crawler system, see "Python data collection " is a good choice (password: 2a69):Click to downloadMachine learning:Network Video recommendation: Wunda Teacher's machine

Summary:Classification and Regression tree (CART) is an important machine learning algorithm that can be used to create a classification tree (classification trees) or to create a regression tree (Regression tree). This paper introduces the principle of cart used for discrete label classification decision and continuous feature regression. The decision tree creation process analyzes the information Chaos Me

to establish a pre-measured model. After the establishment of a model by machine learning algorithm, it is necessary to continuously tune and revise in use, for linear regression. The best model is to obtain the balance between the pre-measured deviation and the model variance (the high deviation is the under-fitting, the high variance is the overfitting). The method of model tuning and correction in linea

The problem of machine learning is divided into supervised learning problems (tagged) and unsupervised learning issues (no tags) depending on whether the question is labeled.Supervised learning can also be divided into regression problems (predictive values are continuous) a

Reprint: http://blog.csdn.net/u012162613/article/details/44261657 This article is part of the third chapter of the overview of neural networks and deep learning, which is a common regularization method in machine learning/depth learning algorithms. (This article will continue to add) regularization method: Prevent ove

, produced by the model.–for regression, is often a sensible measure of the discrepancy.–for classification There is other measures that is generally more sensible (they also work better).Reinforcement learningCombinatorial reinforcement learning, the output is a action or sequence of actions and the only supervisory signal are an Occasiona L scalar reward., haven goal in selecting each action was to maximize the expected sum of the future rewards.–we

"Pattern Recognition and machine learning" ResourcesBishop's "Pattern Recognition and machine learning" is the classic textbook in this field, this article has collected the relevant tutorials and reading notes for comparative learning, the main search resources include CSDN

, and we get the right result. However, do we enter data that is not in the original data set? Let's test two groups:From the data of the two graphs we posted earlier, the data we entered does not exist in the dataset, but the classification is reasonable according to our initial observations.So, this machine learning library is enough for most people. And most despise this library despise that library, tal

, the use of very convenient, greatly reduced the application of machine learning threshold. Of course, the shortcomings are obvious, because of the UDF programming interface provided by the database, the implementation of the algorithm will be subject to a lot of constraints, many optimizations difficult to achieve, and large-scale data sets of