examples of machine learning projects

, the milk is in the same place with the bread sold higher, or with other goods sold higher. Data mining technology can be used to solve such problems. In particular, the store of goods in supermarkets can be divided into related analysis class scene. In daily life, the application of data mining technology is very extensive. For example, for a merchant, it is often necessary to classify their customers ' grades (SVIP, VIP, ordinary customers, etc.), which can be used as training data and part o

market analysis. It is used to build systems similar to those offered by Netflix and Amazon, to recommend products to users based on their purchase history, or to build systems that can find all similar articles for a specific period of time. It can also be used to automatically categorize Web pages based on categories (sports, economics, warfare, etc.), or to mark junk e-mail messages. This article does not fully list all the applications of machine

combat", also take to practice practiced hand, Let your own python step by step, before a variety of web background toss, especially reptiles, but I do not want to help others crawl data, I want to analyze data, mining potential information, the program is a tool, master the business trend is the King!No nonsense, the next series of notes are my coursera above the understanding, according to their handwriting and "machine

This article is a computer Quality Pre-sale recommendation >>>>Spark machine learningWhen machine learning meets the most popular parallel computing framework spark ...Editor's recommendationApache Spark is a distributed computing framework optimized to meet the needs of low latency tasks and memory data storage.Apache Spark is a rare framework in the existing pa

All machine learning models are defective (by John Langford) Attempts to abstract and study machine learning are within some given framework or mathematical model. it turns out that all of these models are significantly flawed for the purpose of studying machine

, data processing.1 defFile2matrix (filename):2FR = open (filename,'R')3Arrayolines =Fr.readlines ()4NumberOfLines =Len (arrayolines)5Returnmat = Zeros ((numberoflines,3))6Classlabelvector = []7index =08 forLineinchArrayolines:9line =Line.strip ()TenListfromline = Line.split (' \ t') OneReturnmat[index,:] = Listfromline[0:3] AClasslabelvector.append (int (listfromline[-1])) -Index + = 1 - returnReturnmat,classlabelvector theRetarnmat,classlabelvector = File2matrix ('Datingdata.txt')When

Author profile: Jie, Etsy data science director, former senior manager of Yahoo Institute. Long-term research work in recommender systems, machine learning and artificial intelligence, published more than 20 papers at top international conferences, and has long served as a member and reviewer of several international conferences and periodicals accreditation committees.Zebian: He Yongcan, Welcome to the fie

first, the integration method(Ensemble Method)The integration approach mainly includesBaggingand theboostingtwo methods,the random forest algorithm is based onBaggingthe idea of machine learning algorithms,in theBaggingin this method, the training data sets are sampled randomly to regroup different datasets, the weak learning algorithm is used to study different

This article introduces several of the most popular machine learning algorithms. There are many machine learning algorithms. The difficulty is to classify methods. Here we will introduce two methods for thinking and classifying these algorithms. The first group of algorithms is the

measurement available cosine formula, etc.), based on the user's rating to recommend (mainly recommended for new users of those products not scored). Specific examples can be found in the Web page: SVD in the recommendation System application.In addition to the SVD decomposition of the actual meaning of each matrix can refer to Google Wu "mathematical Beauty" a book (but personally feel Wu explain UV two matrix when it seems to be reversed, do not kn

This is a creation in Article, where the information may have evolved or changed. Python has become one of the most commonly used languages in artificial intelligence and other related sciences due to its ease of use and its powerful library of tools. Especially in machine learning, is already the most favored language of major projects. In fact, in addition to

Machine learning Feasibility analysis (1)1 , No Free Lunch Machine Learning is not all-powerful, and machine learning is done by learning sample D and speculating about other cases outs

development."Python programming from beginner to Mastery"Yevizong(May 2018)This book is a gradual, easy-to-digest study of the core technology of Python 3 language development, and the implementation process of the specific examples of each knowledge point of the specific use of the process. Through the implementation process of two comprehensive examples, this paper introduces the process of using Python

Machine Learning notes of the Dragon Star program 　Preface In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. This course chooses to talk about the basic mod

A survey of data cleansing and feature processing in machine learning with the increase of the size of the company's transactions, the accumulation of business data and transaction data more and more, these data is the United States as a group buying platform of the most valuable wealth. The analysis and mining of these data can not only provide decision support for the development direction of the American

Original link: http://scikit-learn.github.io/dev/tutorial/basic/tutorial.htmlChapter ContentIn this chapter, we mainly introduce the Scikit-learn machine learning Thesaurus, and will give you a learning sample.Machine Learning: Problem settingIn general, a learning problem i

The ability to give computer learning dataCover:1. General concepts of machine learning2. Three types and basic terminology of machine learning methods3. Modules required to successfully build a machine learning systemThree differ

Http://blog.sina.com.cn/s/blog_6b99cdb50101ix0l.htmlOne of the math related to machine learning and computer vision(The following is a space article to be transferred from an MIT bull, which is very practical:)DahuaIt seems that mathematics is not always enough. These days, in order to solve some of the problems in the library, also held a mathematical textbook. From the university to the present, the class

processes, and statistical reasoning itself can extract useful information from data noise, and the combination will have a better effect. random projection ( Randomized Projection ) is an emerging algorithm in statistical machine learning, which "projects" high-dimensional large datasets into low-dimensional datasets, and does not lose effective information in

Keywords: machine learning, basic terminology, hypothetical spaces, inductive preferences, machine learning usesI. Overview of machine learningMachine learning is a process of computing a model from data , and the resulting model