learn big data from scratch

predicts a number or sequential value, such as the length of a patient's hospitalization or the price of a smartphone.It's easier to remember this:Classification tree output class, regression tree output number.Since we've already talked about how decision trees classify data, we just skip to the chase ...The cart and C4.5 are compared as follows:Is this a supervisory algorithm or an unsupervised one? In order to construct the classification and regr

When big data talks about this, there are a lot of nonsense and useful words. This is far from the implementation of this step. In our previous blog or previous blog, we talked about our position to transfer data from traditional data mining to the Data Platform for processi

Hadoop framework, focus on the provision of one-stop Hadoop solutions, as well as one of the first practitioners of cloud computing's distributed Big Data processing, the avid enthusiast of Hadoop, Constantly in the practice of using Hadoop to solve different areas of big data processing and storage, is now responsibl

slightly inferior to SAS one chip. In the same software as SAS, SPSS personally feel that the amount of data in the hour will be a great use.There is data acquisition, Rcurl and the corresponding crawler technology, and in the big data period, the site in the anti-crawler will certainly do the measures to protect thei

can significantly improve your spark technology capabilities, combat development capabilities, project experience, performance tuning and troubleshooting experience. If the student has already learned "spark from getting started to mastering (Scala programming, Case combat, advanced features, spark kernel source profiling, Hadoop high-end)" Course, then finish this course, you can fully achieve 2-3 years or so of spark big

providing two kinds of data access patterns of SQL and API. For example, the giant Cedar database SEQUOIADB supports other types of data storage formats, including unstructured object storage, while supporting SQL and API access to structured and semi-structured storage. At the same time, the distributed relational database will further enhance convergence, provide a multi-engine storage scheme (Gbase 8a/8

does the data speak? In the era of big data, data not only speak, but also drive business decisions, allowing businesses to move in the right direction. 650) this.width=650; "Src=" Http://s2.51cto.com/wyfs02/M00/85/28/wKioL1ebFEfip18fAACntk9oBpA180.jpg-wh_500x0-wm_3 -wmp_4-s_2417758109.jpg "title=" Zhiyun CRM: The

. Operationalized Time series Analytics databasesPinot–linkedin OLAP data store very similar to Druid.Data AnalysisThe analysis tools range from declarative languages like SQL to procedural languages like Pig. Libraries on the other hand is supporting out of the box implementations of the most common data mining and machine learn ing libraries.ToolsPig–provides a

barsRealTime Druid–a Real time OLAP data store. Operationalized Time series Analytics databases Pinot–linkedin OLAP data store very similar to Druid.Data AnalysisThe analysis tools range from declarative languages like SQL to procedural languages like Pig. Libraries on the other hand is supporting out of the box implementations of the most common data mining and

in arrears than those who are used to cash advances), but they need some extra cash during the holidays, and is willing to pay higher interest.2.4 What must the data contain? The data must containExamples of potentially meaningful results. The purpose of guided data mining is to predict the value of a specific target variable. However, in guided

(Content-based recommendations, collaborative filtering, such as matrix decomposition, etc.)Then test on the public data set to see how the implementation works. A large number of public datasets can be found on the following Web site: UCI machine learning repository/3. Familiar with several open source tools: Weka (for getting started); LIBSVM, Scikit-learn, Shogun4. Take a few 101 races on Kaggle:go from

Share with you what spark is? How to analyze data with spark, and small partners who are interested in big data to learn about it.Big Data Online LearningWhat is Apache Spark?Apache Spark is a cluster computing platform designed for speed and general purpose.From a speed poi

Python financial application programming for big Data projects (data analysis, pricing and quantification investments)Share Network address: https://pan.baidu.com/s/1bpyGttl Password: bt56Content IntroductionThis tutorial introduces the basics of using Python for data analysis and financial application development.Star

well as their respective advantages and disadvantages. It also uses a special chapter to introduce data visualization techniques related to maps. The examples of fresh data (data visualization guide) are rich and illustrated. It is suitable for data analysts, visual designers, and developers interested in

largest, which means I live in Beijing all year round, in Beijing area has accumulated a lot of contacts, Beijing's most friends. On the other hand, it also shows that friends living in Beijing not only learn enthusiasm, but also like to learn and master the more avant-garde knowledge like big data. In addition, the n

Reprint: http://www.cnblogs.com/zhijianliutang/p/4050931.htmlObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft

: Balance tree, AVL tree7th: B + Tree and database indexIv. Fig.8th: The concept and storage of graphs9th: The Traversal of graphs10th: Minimum Spanning tree (MST), prim algorithm, Kruskal algorithm11th: Single source shortest path and Dijkstra algorithm12th: Approximate solution of TSP by genetic algorithmFive: Sort13th: Select Sort, insert sort, hill sort14th: Heap Sort, priority queue15th: Quick Sorting and optimization16th: Merging Sorting and optimization17th: Merge sort and external sort18

consistent, single-digit millisecond latency at any scale.Amazon kinesis--is a real-time platform for streaming data on AWS.datomic--a fully transactional, cloud-enabled, distributed database written in Clojure.There is a word called three people will have my teacher, in fact, as a developer, there is a learning atmosphereIt's especially important to talk to a communication circle. This is my big

network library for iOS and Mac OS x;　　Monkeylearn: Making text mining easier, extracting categorical data from text;　　Nupic: Intelligent Computing Numenta Platform, it is a brain-inspired machine intelligence platform, based on cortical learning algorithm of accurate biological neural network;　　Predictionio: Machine learning server built on Hadoop, Mahout and cascading;　　Samoa: Distributed streaming media machine learning framework;　　Scikit-learn:sc

need to transform the front-end to obtain more dimension, higher frequency and finer granularity data. The data analysis system of Commercial Bank attaches great importance to the storage of business data for a long time, but it does not pay attention to the log of system running state and the collection of personal information of customers, which is the key of