machine learning algorithms ppt

Online looking for an article, for the entry stage or more appropriate, there is some knowledge before some contact, first understand, the specific contact will not be so abrupt.This paper divides machine learning algorithms into 4 categories according to learning methods: supervised

Scikit-learn (formerly Scikits.learn) is a open source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, logistic regre Ssion, naive Bayes, random forests, gradient boosting, K-means and DBSCAN, and is designed-interoperate with the Py

Liblinear instead of LIBSVM 2.Liblinear use, Java version Http://www.cnblogs.com/tec-vegetables/p/4046437.html 3.Liblinear use, official translation. http://blog.csdn.net/zouxy09/article/details/10947323/ http://blog.csdn.net/zouxy09/article/details/10947411 4. Here is an article, write good. Transferred from: http://blog.chinaunix.net/uid-20761674-id-4840097.html For the past more than 10 years, support vector machines (SVM machines) have been the most influential

can be empty if a key does not have a previous state. NewState: Returned by function, also in option form. If an empty option is returned, it indicates that you want to delete the state. The result of Updatestatebykey () is a new dstream, in which the internal RDD sequence is composed of the corresponding (key, state) pairs of each time interval.Next, let's talk about the input source Core Data sources: file streams, including text formats and arbitrary hadoop inp

I. Working methods of machine learning ① Select data: Divide your data into three groups: training data, validating data, and testing data ② model data: Using training data to build models using related features ③ validation Model: Using your validation data to access your model ④ Test Model: Use your test data to check the performance of the validated model ⑤ Use model: Use fully trained models to mak

-3PO, Terminator. There is also a concept of "weak AI" (narrow ai). In short, "weak AI" can accomplish some specific tasks like human beings, possibly better than humans, for example, Pinterest service uses AI to classify images, Facebook uses AI to identify faces, and this is "weak AI". The above example is a case of the actual use of "weak AI", which has embodied some of the characteristics of human intelligence. How to achieve. Where the intelligence comes from. With a question we understand

non-supervised learning:watermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqvdtaxmzq3njq2na==/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/ Dissolve/70/gravity/southeast ">In this way of learning. The input data part is identified, some are not identified, such a learning model can be used to predict, but the model first need to learn the internal structure of the data in order to reasonably organize the data to be

, Terminator.There is also a concept of "weak artificial intelligence (Narrow AI)". In short, "weak AI" can accomplish certain tasks like human beings, possibly better than humans, for example, Pinterest service uses AI to classify images, and Facebook uses AI to recognize faces, which is "weak AI".The above example is a case of "weak artificial intelligence", which already embodies some of the characteristics of human intelligence. How is it achieved? Where does this intelligence come from? Wit

In the previous sections, we have covered what is target detection and how to detect targets, as well as the concepts of sliding windows, bounding box, and IOU, non-maxima suppression.Here will summarize the current target detection research results, and several classical target detection algorithms to summarize, this article is based on deep learning target detection, in the following sections, will be spe

Non-supervised learning: In this learning mode, the input data part is identified, the part is not identified, the learning model can be used for prediction, but the model first needs to learn the internal structure of the data in order to reasonably organize the data to make predictions. The application scenarios include classification and regression, and t

, so as to better identify the problem and adjust the model. The most noteworthy is the feature engineering , the characteristics of the design is often more like an art. In general or to accumulate more, more divergent thinking, hands-on to do, reflect on the summary, gradual.Review of each chapterGetting Started with 1.Python machine learning: This paper introduces the orientation of the book and

The last three weeks of Andrew Ng's machine learning were recently followed by the linear regression (Linear Regression) and logistic regression (logistic Regression) models in machines learning. Make a note here.Also recommended a statistical study of the book, "Statistical Learning method" Hangyuan Li, Book short, on

Use Python to master machine learning in four steps and python to master machines in four steps To understand and apply machine learning technology, you need to learn Python or R. Both are programming languages similar to C, Java, and PHP. However, since Python and R are both relatively young and "Far Away" from the CP

non-spam samples2. Message-based routing information develop a complex set of features3. The development of a series of complex features based on the message body information, including the processing of the truncated words4. Develop complex algorithms for detecting deliberate spelling errors (writing watch as W4tch)Among the options above, it is very difficult to decide which item to spend time and effort on, making wise choices that are better than

lot of things, such as:1. Collect more data and let us have more spam and non-spam samples2. Message-based routing information develop a complex set of features3. The development of a series of complex features based on the message body information, including the processing of the truncated words4. Develop complex algorithms for detecting deliberate spelling errors (writing watch as W4tch)Among the options above, it is very difficult to decide which

from:http://blog.jobbole.com/60809/After understanding the machine learning problems that we need to solve, we can think about what data we need to collect and what algorithms we can use. In this article, we'll go through the most popular machine learning

training sample information, which are not detailed here. 1.3 main tasks of machine learningThe example above describes how machine learning solves the classification problem, and its main task is to divide the instances into appropriate sub - class. Another task of machine learni