coursera machine learning review

This article is the 6th in a series of Python Big Data and machine learning articles that will introduce the NumPy libraries necessary to learn Python big data and machine learning.The knowledge you will be able to learn through this article series is as follows: Using Python for big data and machine

I. Introduction Recently I have written many learning notes about machine learning, which often involves the knowledge of probability theory. Here I will summarize and review all the knowledge about probability theory for your convenience and share it with many bloggers, I hope that with the help of this blog post, you

Based on the literal Relevance Model of Baidu keyword search recommendation tool, this article introduces the specific design and implementation of a machine learning task. Including target setting, training data preparation, feature selection and filtering, and model training and optimization. This model can be extended to Semantic Relevance models, and the design and implementation of Search Engine releva

8. Support Vector machines (SVMs) Content 　　　　8. Support Vector machines (SVMs) 8.1 Optimization Objection 8.2 Large Margin Intuition 8.3 Mathematics Behind Large Margin classification 8.4 Kernels 8.5 Using a SVM 8.5.1 Multi-Class Classification 8.5.2 Logistic Regression vs. SVMs 8.1 Optimization ObjectionSupport Vector Machine (Support Vector MACHINE:SVM) is a very useful supervised

ObjectiveIn the classical machine learning algorithm, the importance of decision tree algorithm must be known to everyone. Whether the ID3 algorithm or the C4.5 algorithm, and so on, are faced with a problem, that is, through the direct generation of the full decision tree for training samples is "over-fitting", plainly is too accurate. This is not the best decision tree to analyze new data, since the full

Learning machine learning algorithms is really a headache, we have so many papers, books, websites can be consulted, they are either refined mathematical description (mathematically), or a step-by-Step text Introduction (textually). If you're lucky enough, you might find some pseudo-code. If the character breaks out, you will even be told how to install it. Howev

, the voting method is used to determine the classification result.In addition to balancing datasets, you can also filter features in different categories and small categories, and then combine them to form a learner. This may also improve the effect.2. Start with the learner. The simplest way is to change the proportion of the category samples by changing the cutoff of the judgment class. You can also learn only one category. In addition, considering the different costs of misjudgment of differ

each match point between the cost and minimum. The results of the match are as follows (Figure III): (Figure III) The above method does not use machine learning, the other part of the search is not an easy thing, because the first to approximate the location of the component, so this method also has shortcomings, but the idea of the deformed part can be used as a feature, and then look at Pedro's second a

transferred from: Http://www.cnblogs.com/LeftNotEasy Author: leftnoteasy regression and gradient descent: Regression in mathematics is given a set of points, can be used to fit a curve, if the curve is a straight line, that is called linear regression, if the curve is a two-time curve, is called two regression, regression there are many variants, such as locally weighted regression, logistic regression , wait, this is going to be in the back. Using a very simple example to illustrate the regr

Chapter 1 Introduction1.1 What are machine learning?T o Solve a problem on a computer, we need an algorithm. An algorithm was a sequence of instructions that should was carried out to transform the input to output. For example, one can devise a algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there is various algorithms and we may be interested in

results, why would they waste their time? In fact, there is no complex conversion or compression, SVM only analyzes the wrong 117/5000 points set (up to 98% accuracy, compared to the DT model is 51%, and GLM model only 12%!) ）Where is the limitation?Many people have doubts, since SVM is so powerful, but why can't we use SVM for everything? Unfortunately, the most magical part of SVM happens to be its biggest weakness! Complex Data transformation information and the results of boundary generatio

After learning SVM, read so many other people's articles, it is time to sum up a wave of their own. The right to write a note for yourself to review it later.PS: Combined with their own in the work process (I use the SVR to do stock prediction) used to write the knowledge, not very comprehensive, if some knowledge is not mentioned here readers can find their own.1 OverviewSupport Vector Machines (SVM) is a

The algorithm we learned today is the KNN nearest neighbor algorithm. KNN is an algorithm for supervised learning classifier classification. Next we will discuss in detail Preface I recently started to learn machine learning. I found a book about machine learning on the Int

Http://product.dangdang.com/23829918.htmlSpark has attracted wide attention as the emerging, most widely used open source framework for big data processing, attracting a lot of programming and developers to learn and develop relevant content, Mllib is the core of the spark framework. This book is a detailed introduction to the Spark mllib program design book, the introduction of simple, rich examples.This book is divided into 12 chapters, starting with the installation and configuration of the S

Fusion Models (Aggregation model)If we've got some features or assumptions, and they have some consistency with our goal of machine learning, we can combine these assumptions to make predictions better, such models are called fusion models.A fusion model is a way to get better predictions by mixing (mix) and combining (combine) some assumptions.The following is a list of four different combinations and give

Recently turned Peter Harrington "machine Learning Combat", see the Logistic regression chapter a little bit of doubt.After a brief introduction of the principle of logistic regression, the author immediately gives the code of the gradient rise algorithm: The range of the algorithm to the jump is a bit large, the author himself said, here omitted a simple mathematical deduction.So in fact, this process is a

Many machine learning algorithms have one hypothesis: input data is linearly divided. The perceptron algorithm must be convergent for completely linearly-divided data. Considering the noise, Adalien, logistic regression, and SVM do not require the data to be completely linearly divided.But there are a lot of non-linear data in real life, and the linear conversion methods such as PCA and LDA are not very goo

0.0 The third is still mathematics, because mathematics is the basis for solving all problems, a question in depth to the last is the support of mathematical knowledge. The so-called basic decision superstructure, such as participation in the ACM competition, the game between the master is not programming skills, more is the mathematical knowledge of the competition. If you want to go far, the mathematical foundation must be played well. Well, it is a pity to learn mathematics before the exam, a

a dependency between them, or through common sense also know that the larger the price of the house, and the goal of linear regression is to find such a "line" (dependency expression) to fit the data.Next summarize the meaning of the downline, the line must be straight line, but not necessarily a two-dimensional straight line, can be multidimensional straight line, can not be the other shape of the line (curved), but also need to distinguish between the point and polygon. Blog structureThe co

Similarity measurement in machine learning, Comparison of method summaryai lin 1 weeks ago (01-10) 876 ℃ 0 Reviews CangwuWhen classifying, it is often necessary to estimate the similarity metric between different samples (similarity measurement), which is usually done by calculating the "distance" (Distance) between samples. The method used to calculate the distance is very fastidious, even related to the